1. |
- Abdi, A. M., et al.
(författare)
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First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
- 2019
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 78, s. 249-260
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Tidskriftsartikel (refereegranskat)abstract
- The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO 2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO 2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness (T-G) model, the greenness and radiation (GöR) model and a light use efficiency model (MOD17). The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3–65%). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation (R 2 ), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R 2 = 0.73, RMSE = 1.45 g C m −2 d −1 , and BIC = 678; the T-G model produced R 2 = 0.68, RMSE = 1.57 g C m −2 d −1 , and BIC = 707; the MOD17 model produced R 2 = 0.49, RMSE = 1.98 g C m −2 d −1 , and BIC = 800. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models (R 2 = 0.77, RMSE = 1.32 g C m −2 d −1 , and BIC = 631). These results show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.
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2. |
- Abid, Nosheen, 1993-, et al.
(författare)
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UCL: Unsupervised Curriculum Learning for Water Body Classification from Remote Sensing Imagery
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 105
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.
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3. |
- Araza, Arnan, et al.
(författare)
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Past decade above-ground biomass change comparisons from four multi-temporal global maps
- 2023
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - 1569-8432. ; 118
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Tidskriftsartikel (refereegranskat)abstract
- Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (ΔAGB) products has increased in recent years. Here we assessed the past decade net ΔAGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local ΔAGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall ΔAGB map cross-correlation between maps varied in the range 0.11–0.29 (r). Reported ΔAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that ΔAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, ΔAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.
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4. |
- Ardö, Jonas, et al.
(författare)
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MODIS EVI-based net primary production in the Sahel 2000–2014
- 2018
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 65, s. 35-45
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Tidskriftsartikel (refereegranskat)abstract
- Africa is facing resource problems due to increasing demand combined with potential climate-induced changes in supply. Here we aim to quantify resources in terms of net primary production (NPP [g C m−2 yr−1]) of vegetation in the Sahel region for 2000–2014.Using time series of the enhanced vegetation index (EVI) from MODIS, NPP was estimated for the Sahel region with a 500 × 500 m spatial resolution and 8-day temporal resolution. The estimates were based on local eddy covariance flux measurements from six sites in the Sahel region and the carbon use efficiency originating from a dynamic vegetation model.No significant NPP change was found for the Sahel as a region but, for sub-regions, significant changes, both increasing and decreasing, were observed. Substantial uncertainties related to NPP estimates and the small availability of evaluation data makes verification difficult. The simplicity of the methodology used, dependent on earth observation only, is considered an advantage.
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5. |
- Axelsson, Arvid, et al.
(författare)
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Tree species classification using Sentinel-2 imagery and Bayesian inference
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434. ; 100
-
Tidskriftsartikel (refereegranskat)abstract
- The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58°27′18.35″ N, 13°39′8.03″ E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen’s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously.
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6. |
- Axelsson, Christoffer, et al.
(författare)
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The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest
- 2023
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 118
-
Tidskriftsartikel (refereegranskat)abstract
- The estimation of species composition and species-specific stem volumes are critical components of many forest inventories. The use of airborne laser scanning with multiple spectral channels may prove instrumental for the cost-efficient retrieval of these forest variables. In this study, we scanned a boreal forest using two channels: 532 nm (green) and 1064 nm (near infrared). The data was used in a two-step methodology to (1) classify species, and (2) estimate species-specific stem volume at the level of individual tree crowns. The classification of pines, spruces and broadleaves involved linear discriminant analysis (LDA) and resulted in an overall accuracy of 91.1 % at the level of individual trees. For the estimation of stem volume, we employed species-specific k-nearest neighbors models and evaluated the performance at the plot level for 256 field plots located in central Sweden. This resulted in root-mean-square errors (RMSE) of 36 m3/ha (16 %) for total volume, 40 m3/ha (27 %) for pine volume, 32 m3/ha (48 %) for spruce volume, and 13 m3/ha (87 %) for broadleaf volume. We also simulated the use of a monospectral near infrared (NIR) scanner by excluding features based on the green channel. This resulted in lower overall accuracy for the species classification (86.8 %) and an RMSE of 41 m3/ha (18 %) for the estimation of total stem volume. The largest difference when only the NIR channel was used was the difficulty to accurately identify broadleaves and estimate broadleaf stem volume. When excluding the green channel, RMSE for broadleaved volume increased from 13 to 26 m3/ha. The study thus demonstrates the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. In addition, we investigated how tree height influences the results where shorter trees were found to be more difficult to classify correctly.
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7. |
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8. |
- Bhardwaj, Anshuman, et al.
(författare)
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A review on remotely sensed land surface temperature anomaly as an earthquake precursor
- 2017
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 63, s. 158-166
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Tidskriftsartikel (refereegranskat)abstract
- The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.
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9. |
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10. |
- Bohlin, Inka, et al.
(författare)
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Quantifying post-fire fallen trees using multi-temporal lidar
- 2017
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 63, s. 186-195
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Tidskriftsartikel (refereegranskat)abstract
- Massive tree-felling due to root damage is a common fire effect on burnt areas in Scandinavia, but has so far not been analyzed in detail. Here we explore if pre- and post-fire lidar data can be used to estimate the proportion of fallen trees. The study was carried out within a large (14,000 ha) area in central Sweden burnt in August 2014, where we had access to airborne lidar data from both 2011 and 2015. Three data-sets of predictor variables were tested: POST (post-fire lidar metrics), D1F (difference between post- and pre-fire lidar metrics) and combination of those two (POST_DIF). Fractional logistic regression was used to predict the proportion of fallen trees. Training data consisted of 61 plots, where the number of fallen and standing trees was calculated both in the field and with interpretation of drone images. The accuracy of the best model was tested based on 100 randomly selected validation plots with a size of 25 x 25 m.Our results showed that multi-temporal lidar together with field-collected training data can be used for quantifying post-fire tree felling over large areas. Several height-, density- and intensity metrics correlated with the proportion of fallen trees. The best model combined metrics from both datasets (POST DIF), resulting in a RMSE of 0.11. Results were slightly poorer in the validation plots with RMSE of 0.18 using pixel size of 12.5 m and RMSE of 0.15 using pixel size of 6.25 m. Our model performed least well for stands that had been exposed to high-intensity crown fire. This was likely due to the low amount of echoes from the standing black tree skeletons. Wall-to-wall maps produced with this model can be used for landscape level analysis of fire effects and to explore the relationship between fallen trees and forest structure, soil type, fire intensity or topography.
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11. |
- Georganos, Stefanos, et al.
(författare)
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A census from heaven : Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments
- 2022
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- Urban population distribution maps are vital elements for monitoring the Sustainable Development Goals, appropriately allocating resources such as vaccination campaigns, and facilitating evidence-based decision making. Typically, population distribution maps are derived from census data from the region of interest. Nevertheless, in several low-and middle-income countries, census information may be unreliable, outdated or unsuitable for spatial analysis at the intra-urban level, which poses severe limitations in the development of urban population maps of adequate quality. To address these shortcomings, we deploy a novel framework utilizing multisource Earth Observation (EO) information such as Sentinel-2 and very-high-resolution Pleiades imagery, openly available building footprint datasets, and deep learning (DL) architectures, providing end -to-end solutions to the production of high quality intra-urban population distribution maps in data scarce contexts. Using several case studies in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania), our results emphasize that the combination of DL and EO data is very potent and can successfully capture relationships between the retrieved image features and population counts at fine spatial resolutions (100 meter). Moreover, for the first time, we used state-of-the-art domain adaptation methods to predict population distributions in Dar es Salaam and Nairobi (R2 = 0.39, 0.60) that did not require national census or survey data from Kenya or Tanzania, but only a sample of training locations from Dakar. The DL architecture is based on a modified ResNet-18 model with dual-streams to analyze multi-modal data. Our findings have strong implications for the development of a new generation of urban population products that are an output of end-to-end solutions, can be updated frequently and rely completely on open data.
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12. |
- Haas, Jan, et al.
(författare)
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Satellite monitoring of urbanization and environmental impacts : A comparison of Stockholm and Shanghai
- 2015
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 38, s. 138-149
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates urbanization and its potential environmental consequences in Shanghai andStockholm metropolitan areas over two decades. Changes in land use/land cover are estimated fromsupport vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix fea-tures. Landscape metrics are used to investigate changes in landscape composition and configurationand to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbaniza-tion is calculated by urbanization indices and the resulting impacts on the environment are quantified byecosystem services. Growth of urban areas and urban green spaces occurred at the expense of croplandin both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120%in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover changewas a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results,it appears that fragmentation in both study regions occurred mainly due to the growth of high densitybuilt-up areas in previously more natural/agricultural environments, while the expansion of low densitybuilt-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted inecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to thedecrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very lit-tle. Total urban growth in Shanghai was 1768 km2and 100 km2in Stockholm. The developed methodologyis considered a straight-forward low-cost globally applicable approach to quantitatively and qualitativelyevaluate urban growth patterns that could help to address spatial, economic and ecological questions inurban and regional planning.
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13. |
- Haas, Jan, 1983-, et al.
(författare)
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Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 30:1, s. 42-55
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates land cover changes, magnitude and speed of urbanization and evaluates possible impacts on the environment by the concepts of landscape metrics and ecosystem services in China's three largest and most important urban agglomerations: Jing-Jin-Ji, the Yangtze River Delta and the Pearl River Delta. Based on the classifications of six Landsat TM and HJ-1A/B remotely sensed space-borne optical satellite image mosaics with a superior random forest decision tree ensemble classifier, a total increase in urban land of about 28,000 km(2) could be detected alongside a simultaneous decrease in natural land cover classes and cropland. Two urbanization indices describing both speed and magnitude of urbanization were derived and ecosystem services were calculated with a valuation scheme adapted to the Chinese market based on the classification results from 1990 and 2010 for the predominant land cover classes affected by urbanization: forest, cropland, wetlands, water and aquaculture. The speed and relative urban growth in Jing-Jin-Ji was highest, followed by the Yangtze River Delta and Pearl River Delta, resulting in a continuously fragmented landscape and substantial decreases in ecosystem service values of approximately 18.5 billion CNY with coastal wetlands and agriculture being the largest contributors. The results indicate both similarities and differences in urban-regional development trends implicating adverse effects on the natural and rural landscape, not only in the rural-urban fringe, but also in the cities' important hinterlands as a result of rapid urbanization in China.
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14. |
- Hinsby, Klaus, et al.
(författare)
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Mapping and understanding Earth : Open access to digital geoscience data and knowledge supports societal needs and UN sustainable development goals
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 130
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Tidskriftsartikel (refereegranskat)abstract
- Open access to harmonised digital data describing Earth 's surface and subsurface holds immense value for society. This paper highlights the significance of open access to digital geoscience data ranging from the shallow topsoil or seabed to depths of 5 km. Such data play a pivotal role in facilitating endeavours such as renewable geoenergy solutions, resilient urban planning, supply of critical raw materials, assessment and protection of water resources, mitigation of floods and droughts, identification of suitable locations for carbon capture and storage, development of offshore wind farms, disaster risk reduction, and conservation of ecosystems and biodiversity. EuroGeoSurveys, the Geological Surveys of Europe, have worked diligently for over a decade to ensure open access to harmonised digital European geoscience data and knowledge through the European Geological Data Infrastructure (EGDI). EGDI acts as a data and information resource for providing wide-ranging geoscience data and research, as this paper demonstrates through selected research data and information on four vital natural resources: geoenergy, critical raw materials, water, and soils. Importantly, it incorporates near realtime remote and in-situ monitoring data, thus constituting an invaluable up -to -date database that facilitates informed decision-making, policy implementation, sustainable resource management, the green transition, achieving UN Sustainable Development Goals (SDGs), and the envisioned future of digital twins in Earth sciences. EGDI and its thematic map viewer are tailored, continuously enhanced, and developed in collaboration with all relevant researchers and stakeholders. Its primary objective is to address societal needs by providing data for sustainable, secure, and integrated management of surface and subsurface resources, effectively establishing a geological service for Europe. We argue that open access to surface and subsurface geoscience data is crucial for an efficient green transition to a net -zero society, enabling integrated and coherent surface and subsurface spatial planning.
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15. |
- Hu, Xikun, 1994-, et al.
(författare)
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Sentinel-2 MSI data for active fire detection in major fire-prone biomes : A multi-criteria approach
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 101
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Tidskriftsartikel (refereegranskat)abstract
- Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial and temporal coverage for monitoring biomass burning which could complement other coarse active fire detection products. This paper aims to investigate the use of reflective wavelength Sentinel-2 data to classify unambiguous active fire areas from inactive areas at 20 m spatial resolution. A multi-criteria approach based on the reflectance of several bands (i.e. B4, B11, and B12) is proposed to demonstrate the boundary constraints in several representative biomes. It is a fully automatic algorithm based on adaptive thresholds that are statistically determined from 11 million Sentinel-2 observations acquired over corresponding summertime (June 2019 to September 2019) across 14 regions or countries. Biome-based parameterizations avoid high omission errors (OE) caused by small and cool fires in different landscapes. It also takes advantage of the multiple criteria whose intersection could reduce the potential commission errors (CE) due to soil dominated pixels or highly reflective building rooftops. Active fire detection performance was mainly evaluated through visual inspection on eight illustrative subsets because of unavailable ground truth. The detection results revealed that CE and OE could be kept at a low level with 0.14 and 0.04 as an acceptable trade-off. The proposed algorithm can be employed for rapid active fire detection as soon as the image is obtained without the requirement of using multi-temporal imagery, and can even be adapted to onboard processing in the future.
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16. |
- Huesca, Margarita, et al.
(författare)
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Ecosystem functional assessment based on the "optical type" concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation
- 2015
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 43, s. 132-148
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Tidskriftsartikel (refereegranskat)abstract
- Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of "optical types" (OT), meaning "optically distinguishable functional types", as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation. This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MOD IS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found. Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability. (C) 2015 Elsevier B.V. All rights reserved.
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17. |
- Joseph, Shijo, et al.
(författare)
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Comparison of carbon assimilation estimates over tropical forest types in India based on different satellite and climate data products
- 2012
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 18, s. 557-563
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Tidskriftsartikel (refereegranskat)abstract
- Carbon assimilation defined as the overall rate of fixation of carbon through the process of photosynthesis is central to the climate change research. The present study compares the two well-known algorithms in satellite based carbon assimilation estimation, the Vegetation Photosynthesis Model (VPM) and the MOD 17A2 GPP Model, over the tropical forest types in India for a period of two years (September, 2006-August, 2008). The results indicate that the evergreen forest assimilate carbon at a higher rate while the rate is lower for montane grasslands. The comparison between the model results shows that there are large differences between these estimates, and that the spatial resolution of the input datasets plays a larger role than the algorithms of the models. The comparison exercise will be helpful for the refinement and development of the existing and future GPP models by incorporating the empirical environmental conditions. (C) 2011 Elsevier B.V. All rights reserved.
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18. |
- Karlson, Martin, et al.
(författare)
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Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species
- 2016
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434 .- 1872-826X. ; 50:August, s. 80-88
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Tidskriftsartikel (refereegranskat)abstract
- High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA = 78.4%) proved to be more suitable than the wet season (OA = 68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore concluded that WorldView- 2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands.
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19. |
- Knudby, Anders, et al.
(författare)
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Using multiple Landsat scenes in an ensemble classifier reduces classification error in a stable nearshore environment
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 28, s. 90-101
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Tidskriftsartikel (refereegranskat)abstract
- Medium-scale land cover maps are traditionally created on the basis of a single cloud-free satellite scene, leaving information present in other scenes unused. Using 1309 field observations and 20 cloud- and error-affected Landsat scenes covering Zanzibar Island, this study demonstrates that the use of multiple scenes can both allow complete coverage of the study area in the absence of cloud-free scenes and obtain substantially improved classification accuracy. Automated processing of individual scenes includes derivation of spectral features for use in classification, identification of clouds, shadows and the land/water boundary, and random forest-based land cover classification. An ensemble classifier is then created from the single-scene classifications by voting. The accuracy achieved by the ensemble classifier is 70.4%, compared to an average of 62.9% for the individual scenes, and the ensemble classifier achieves complete coverage of the study area while the maximum coverage for a single scene is 1209 of the 1309 field sites. Given the free availability of Landsat data, these results should encourage increased use of multiple scenes in land cover classification and reduced reliance on the traditional single-scene methodology.
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20. |
- Kratzer, Susanne, et al.
(författare)
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Integrating mooring and ship-based data for improved validation of OLCI chlorophyll-a products in the Baltic Sea
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 94
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Tidskriftsartikel (refereegranskat)abstract
- A Water Quality Monitor (WQM) equipped with a range of oceanographic sensors was deployed from April 2017 to October 2017 in the North Western (NW) Baltic Sea. We assessed here if the data from a moored chlorophyll-a fluorometer can be used to improve satellite validation in coastal waters. Calibrated mooring data and ship-based chlorophyll-a concentrations from 2017 and 2018 were matched with OLCI (Ocean and Land Colour Instrument) data to validate the C2RCC (Case-2 Regional Coast Colour) processor, a locally-adapted version of C2RCC (LA-C2RCC), as well as the POLYMER processor. Using additional mooring data resulted in a substantial increase in paired observations compared to using ship-based data alone (C2RCC; N = 41-63, LA-C2RCC; N = 37-59, POLYMER; N = 108-166). However, the addition of mooring data only reduced the error and bias of the LA-C2RCC (MNB: from 24 % to 22 %, RMSE: from 60 % to 57 %, APD: both 47 %). In contrast, the statistical errors increased with the addition of mooring data both for C2RCC (MNB: -26 % to -33 %, RMSE: 50 %-51 %, APD 84 %-96 %) and for POLYMER (MNB: 26 %-36 %, RMSE: 79 % to 79 %, APD 64 %-64 %). The results indicate that the locally-adapted version of the C2RCC should be used for the area of investigation. These results are most likely also related to the effect of the System Vicarious Calibration (SVC). As opposed to C2RCC, the locally-adapted version had not been vicariously calibrated. The results indicate that SVC is not beneficial for Baltic Sea data and that more work needs to be done to improve SVC for Baltic Sea waters or for other waters with high CDOM absorption. In order to improve the validation capabilities of moored fluorometers in general, they should be strategically placed in waters with representative ranges of chl-a concentrations for the area of research in question.
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21. |
- Lindberg, Eva, et al.
(författare)
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Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method
- 2021
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 100
-
Tidskriftsartikel (refereegranskat)abstract
- Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.
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22. |
- Martínez, B., et al.
(författare)
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Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products
- 2018
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 65, s. 124-136
-
Tidskriftsartikel (refereegranskat)abstract
- The main goal of this paper is to derive a method for a daily gross primary production (GPP) product over Europe and Africa taking the full advantage of the SEVIRI/MSG satellite products from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors delivered from the Satellite Application Facility for Land Surface Analysis (LSA SAF) system. Special attention is paid to model the daily GPP response from an optimized Montheith's light use efficiency model under dry conditions by controlling water shortage limitations from the actual evapotranspiration and the potential evapotranspiration (PET). The PET was parameterized using the mean daily air temperature at 2 m (Ta) from ERA-Interim data. The GPP product (MSG GPP) was produced for 2012 and assessed by direct site-level comparison with GPP from eddy covariance data (EC GPP). MSG GPP presents relative bias errors lower than 40% for the most forest vegetation types with a high agreement (r > 0.7) when compared with EC GPP. For drylands, MSG GPP reproduces the seasonal variations related to water limitation in a good agreement with site level GPP estimates (RMSE = 2.11 g m−2 day−1; MBE = −0.63 g m−2 day−1), especially for the dry season. A consistency analysis against other GPP satellite products (MOD17A2 and FLUXCOM) reveals a high consistency among products (RMSD < 1.5 g m−2 day−1) over Europe, North and South Africa. The major GPP disagreement arises over moist biomes in central Africa (RMSD > 3.0 g m−2 day−1) and over dry biomes with MSG GPP estimates lower than FLUXCOM (MBD up to −3.0 g m−2 day−1). This newly derived product has the potential for analysing spatial patterns and temporal dynamics of GPP at the MSG spatial resolutions on a daily basis allowing to better capture the GPP dynamics and magnitude.
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23. |
- Martinez Garcia, Eduardo
(författare)
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Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds
- 2022
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 113
-
Tidskriftsartikel (refereegranskat)abstract
- Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at dielmo@dielmo.com.
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24. |
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25. |
- Montaghi, Alessandro, et al.
(författare)
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Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study
- 2013
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 23, s. 288-300
-
Forskningsöversikt (refereegranskat)abstract
- This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches such as single tree detection and area-based modeling have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems. (C) 2012 Elsevier B.V. All rights reserved.
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26. |
- Montaghi, Alessandro, et al.
(författare)
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Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
- 2013
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 25, s. 87-97
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km(2) and 487 km(2) in Sicily (Italy), 16,761 plots of size 30-m x 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns. (C) 2013 Elsevier B.V. All rights reserved.
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27. |
- Mugiraneza, Theodomir, et al.
(författare)
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Monitoring urbanization and environmental impact in Kigali, Rwanda using Sentinel-2 MSI data and ecosystem service bundles
- 2022
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 109
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Tidskriftsartikel (refereegranskat)abstract
- Rapid urbanization in developing countries often results in uncontrolled urban growth. In order to support sustainable urban development, reliable and up-to-date information on urban land cover changes and their environmental impact is needed. In this study, we aim at evaluating the potential of Sentinel-2 (S-2) Multi-spectral Instrument (MSI) data for urban land cover change monitoring and for analyzing resulting impacts on Ecosystem Services (ES) provision in Kigali, Rwanda. Land cover classification into eight distinct urban classes (84% overall accuracies, 0.8 Kappa) was performed on data from 2016 and 2021 using a hybrid approach combining Random Forest with a U-Net-based impervious surface segmentation that improved the delineation of urban areas. The bi-temporal land cover maps were then analyzed regarding landscape structure using Landscape Metrics (LM). Ecosystem service bundles were derived for both years and their changes were summarized. Service providing areas were further evaluated in terms of changes in spatial attributes and structure of patches. ES were aggregated into eight bundles and grouped into provisioning, regulating and supporting services. The bundles were further analyzed using a matrix spatially linking landscape units with service supply and demand budgets. The results illustrated that three urban development scenarios can be distinguished including infill through housing and infrastructures development in core urban areas, urban sprawl in fringe zones and the development of urban patches at distant locations intercepted by cropland. The results revealed that the changes in LM negatively affected ES supply mainly through a decrease in cropland and forest. The expansion of built-up areas resulted in a high demand for provisioning and regulating services, especially food and water provision, surface runoff mitigation and erosion control. This is the first study demonstrating that detailed monitoring of urbanization and resulting environmental impacts can be performed with open access S-2 MSI data in Sub-Saharan Africa. Moreover, the framework developed in this study has the potential to be transferred to other Sub-Saharan cities.
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28. |
- Musenge, Eustasius, et al.
(författare)
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Bayesian analysis of zero inflated spatiotemporal HIV/TB child mortality data through the INLA and SPDE approaches : applied to data observed between 1992 and 2010 in rural North East South Africa
- 2013
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 22, s. 86-98
-
Tidskriftsartikel (refereegranskat)abstract
- Longitudinal mortality data with few deaths usually have problems of zero-inflation. This paper presents and applies two Bayesian models which cater for zero-inflation, spatial and temporal random effects. To reduce the computational burden experienced when a large number of geo-locations are treated as a Gaussian field (GF) we transformed the field to a Gaussian Markov Random Fields (GMRF) by triangulation. We then modelled the spatial random effects using the Stochastic Partial Differential Equations (SPDEs). Inference was done using a computationally efficient alternative to Markov chain Monte Carlo (MCMC) called Integrated Nested Laplace Approximation (INLA) suited for GMRF. The models were applied to data from 71,057 children aged 0 to under 10 years from rural north-east South Africa living in 15,703 households over the years 1992-2010. We found protective effects on HIV/TB mortality due to greater birth weight, older age and more antenatal clinic visits during pregnancy (adjusted RR (95% CI)): 0.73(0.53;0.99), 0.18(0.14;0.22) and 0.96(0.94;0.97) respectively. Therefore childhood HIV/TB mortality could be reduced if mothers are better catered for during pregnancy as this can reduce mother-to-child transmissions and contribute to improved birth weights. The INLA and SPDE approaches are computationally good alternatives in modelling large multilevel spatiotemporal GMRF data structures.
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29. |
- Nyström, Mattias, et al.
(författare)
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Detection of windthrown trees using airborne laser scanning
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 30, s. 21-29
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a method has been developed for the detection of windthrown trees under a forest canopy, using the difference between two elevation models created from the same high density (65 points/m(2)) airborne laser scanning data. The difference image showing objects near the ground was created by subtracting a standard digital elevation model (DEM) from a more detailed DEM created using an active surface algorithm. Template matching was used to automatically detect windthrown trees in the difference image. The 54 ha study area is located in hemi-boreal forest in southern Sweden (Lat. 58 degrees 29' N, Long. 13 degrees 38' E) and is dominated by Norway spruce (Picea abies) with 3.5% deciduous species (mostly birch) and 1.7% Scots pine (Pinus sylvestris). The result was evaluated using 651 field measured windthrown trees. At individual tree level, the detection rate was 38% with a commission error of 36%. Much higher detection rates were obtained for taller trees; 89% of the trees taller than 27 m were detected. For pine the individual tree detection rate was 82%, most likely due to the more easily visible stem and lack of branches. When aggregating the results to 40 m square grid cells, at least one tree was detected in 77% of the grid cells which according to the field measurements contained one or more windthrown trees. (C) 2014 Elsevier B.V. All rights reserved.
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30. |
- O'Connell, J., et al.
(författare)
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A monitoring protocol for vegetation change on Irish peatland and heath
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 31, s. 130-142
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Tidskriftsartikel (refereegranskat)abstract
- Amendments to Articles 3.3 and 3.4 of the Kyoto Protocol have meant that detection of vegetation change may now form an interracial part of national soil carbon stocks. In this study multispectral multi-platform satellite data was processed to detect change to the surface vegetation of four peatland sites and one heath in Ireland. Spectral and spatial thresholds were used on difference images between master and slave data in the extraction of temporally invariant targets for multi-platform cross calibration. The Kolmogorov-Smirnov test was used to evaluate any difference in the cumulative probability distributions of the master, slave and calibrated slave data as expressed by the D statistic, with values reduced by an average of 89.7% due to the cross calibration procedure. A change detection model was created which incorporated a spatial threshold of 9 pixels and a standard deviation (SD) spectral threshold. Kappa accuracy values for the five sites ranged from 80 to 97%, showing that 1.5 SD was the optimum spectral threshold for detecting vegetation change. Change detection results showed mean percentage change ranging from 2.11 to 3.28% of total area and cumulative change over the observed time period of between 15.24 and 49.27% of total area. (C) 2014 Elsevier B.V. All rights reserved.
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31. |
- Peña, Francisco J., 1987-, et al.
(författare)
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DEEPAQUA : Semantic segmentation of wetland water surfaces with SAR imagery using deep neural networks without manually annotated data
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 126
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a deep learning model inspired by knowledge distillation (a.k.a. teacher–student model) to generate labeled data automatically and eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques for water detection by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 3%, Intersection Over Union by 11%, and F1-score by 6%. This approach offers a practical solution for monitoring wetland water extent changes without the need of ground truth data, making it highly adaptable and scalable for wetland monitoring.
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32. |
- Persson, Henrik, et al.
(författare)
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Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume
- 2021
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 96
-
Tidskriftsartikel (refereegranskat)abstract
- In this study, data from the satellite sensors TanDEM-X and Sentinel-2 were combined with national field inventory data to predict forest above-ground biomass (AGB) and stem volume (VOL) over a large area in Sweden. The data sources were evaluated both separately and in combination. The study area covers approximately 20,000,000 ha and corresponds to about 70% of the Swedish forest area. The study area was divided into tiles of 2.5 x 2.5 km(2), which were processed sequentially. The field plots were inventoried on 7 m and 10 m circular plots by the Swedish National Forest Inventory, and plot AGB and VOL at the year of the satellite data were estimated based on a 10-year period of field data. The AGB and VOL were modelled using the k nearest neighbor (kNN) algorithm, with k = 5 neighbors. The combined use of two data sources with different scene extents enabled the generation of seamless AGB and VOL maps. Moreover, the kNN algorithm provided the VOL divided per tree species, which was used for classification of the dominant tree species at stand-level. The overall accuracy for the dominant tree species classification was 77%. The predicted AGB and VOL rasters were evaluated using 549 field inventoried forest stands distributed over Sweden. The RMSE for the predictions based on both data sources were 31.4 t/ha (29.1%) for AGB, and 59.0 m(3)/ha (30.2%) for VOL. By estimating and removing the variance due to sampling (the stand values were estimated from sample plots), the RMSE was improved to 18.0 t/ ha (16.6%). The evaluated approach of using kNN was suitable for estimating forest variables from a combination of different satellite sensors, provided sufficient field reference data are available. The TanDEM-X data were most important for the AGB and VOL predictions, while Sentinel-2 data were essential to map the tree species.
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33. |
- Persson, Henrik, et al.
(författare)
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Forest biomass retrieval approaches from earth observation in different biomes
- 2019
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 77, s. 53-68
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Tidskriftsartikel (refereegranskat)abstract
- The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha(-1) to 55 t ha(-1) (37% to 67% relative RMSE), and an overall bias ranging from -1 t ha(-1) to +5 t ha(-1) at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha(-1)) in the lower AGB classes, and underestimation (up to 85 t ha(-1)) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.
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34. |
- Pitkänen, Timo P., et al.
(författare)
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Detecting subpixel deciduous components to complement traditional land cover classifications in Southwest Finland
- 2015
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 42, s. 97-105
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Tidskriftsartikel (refereegranskat)abstract
- To ensure successful conservation of ecological and cultural landscape values, detailed and up-to-datespatial information of existing habitat patterns is essential. However, traditional satellite-based and rasterclassifications rely on pixels that are assigned to a single category and often generalized. For many frag-mented key habitats, such a strategy is too coarse and complementary data is needed. In this paper,we aim at detecting pixel-wise fractional coverage of broadleaved woodland and grassland componentsin a hemiboreal landscape. This approach targets ecologically relevant deciduous fractions and com-plements traditional crisp land cover classifications. We modeled fractional components using a k-NNapproach, which was based on multispectral satellite data, assisted by a digital elevation model and acontemporary map database. The modeled components were then analyzed based on landscape struc-ture indicators, and evaluated in conjunction with CORINE classification. The results indicate that bothbroadleaved forest and grassland components are widely distributed in the study area, principally orga-nized as transition zones and small patches. Landscape structure indicators show a substantial variationbased on the fractional threshold, pinpointing their dependency on the classification scheme and grain.The modeled components, on the other hand, suggest high internal variation for most CORINE classes,indicating their heterogeneous appearance and showing that the presence of deciduous components inthe landscape are not properly captured in a coarse land cover classification. To gain a realistic perceptionof the landscape, and use this information for the needs of spatial planning, both fractional results andexisting land cover classifications are needed. This is because they mutually contribute to an improvedunderstanding of habitat patterns and structures, and should be used to complement each other.
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35. |
- Reese, Heather, et al.
(författare)
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Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 27, s. 81-90
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Tidskriftsartikel (refereegranskat)abstract
- Climate change and outdated vegetation maps are among the reasons for renewed interest in mapping sensitive alpine and subalpine vegetation. Satellite data combined with elevation derivatives have been shown to be useful for mapping alpine vegetation, however, there is room for improvement.The inclusion of airborne laser scanning data metrics has not been widely investigated for alpine vegetation. This study has combined SPOT 5 satellite data, elevation derivatives, and laser data metrics for a 25 km x 31 km study area in Abisko, Sweden. Nine detailed vegetation classes defined by height, density and species composition in addition to snow/ice, water, and bare rock were classified using a supervised Random Forest classifier. Several of the classes consisted of shrub and grass species with a maximum height of 0.4 m or less. Laser data metrics were calculated from the nDSM based on a 10 m x 10 m grid, and after variable selection, the metrics used in the classification were the 95th and 99th height percentiles, a vertical canopy density metric, the mean and standard deviation of height, a vegetation ratio based on the raw laser data point cloud with a variable height threshold (from 0.1 to 1.0 m with 0.1 m intervals), and standard deviation of these vegetation ratios. The satellite data used in classification was all SPOT bands plus NDVI and NDII, while the elevation derivatives consisted of elevation, slope and the Saga Wetness Index. Overall accuracy when using the combination of laser data metrics, elevation derivatives and SPOT 5 data increased by 6% as compared to classification of SPOT and elevation derivatives only, and increased by 14.2% compared to SPOTS data alone. The classes which benefitted most from inclusion of laser data metrics were mountain birch and alpine willow. The producer's accuracy for willow increased from 18% (SPOT alone) to 41% (SPOT + elevation derivatives) and then to 55% (SPOT + elevation derivatives + laser data) when laser data were included, with the 95th height percentile and Saga Wetness Index contributing most to willow's improved classification. Addition of laser data metrics did not increase the classification accuracy of spectrally similar dry heath (< 0.3 m height) and mesic heath (0.3-1.0 m height), which may have been a result of laser data penetration of sparse shrub canopy or laser data processing choices. The final results show that laser data metrics combined with satellite data and elevation derivatives contributed overall to a better classification of alpine and subalpine vegetation. (c) 2013 Elsevier B.V. All rights reserved.
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36. |
- Rudke, Anderson Paulo, et al.
(författare)
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Land cover data of Upper Parana River Basin, South America, at high spatial resolution
- 2019
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 83
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Tidskriftsartikel (refereegranskat)abstract
- This study presents a new land cover map for the Upper Paraná River Basin (UPRB-2015), with high spatial resolution (30 m), and a high number of calibration and validation sites. To the new map, 50 Landsat-8 scenes were classified with the Support Vector Machine (SVM) algorithm and their level of agreement was assessed using overall accuracy and Kappa coefficient. The generated map was compared by area and by pixel with six global products (MODIS, GlobCover, Globeland30, FROM-GLC, CCI-LC and, GLCNMO). The results of the new classification showed an overall accuracy ranging from 67% to 100%, depending on the sub-basin (80.0% for the entire UPRB). Kappa coefficient was observed ranging from 0.50 to 1.00 (average of 0.73 in the whole basin). Anthropic areas cover more than 70% of the entire UPRB in the new product, with Croplands covering 46.0%. The new mapped areas of croplands are consistent with local socio-economic statistics but don't agree with global products, especially FROM-GLC (14,9%), MODIS (33.8%), GlobCover (71.2%), and CCI (67.8%). In addition, all global products show generalized spatial disagreement, with some sub-basins showing areas of cropland varying by an order of magnitude, compared to UPRB-2015. In the case of Grassland, covering 25.6% of the UPRB, it was observed a strong underestimation by all global products. Even for the Globeland30 and MODIS, which show some significant fraction of pasture areas, there is a high level of disagreement in the spatial distribution. In terms of general agreement, the seven compared mappings (including the new map) agree in only 6.6% of the study area, predominantly areas of forest and agriculture. Finally, the new classification proposed in this study provides better inputs for regional studies, especially for those involving hydrological modeling as well as offers a more refined LU/LC data set for atmospheric numerical models.
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37. |
- Sang, Yirong, et al.
(författare)
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Assessing topographic effects on forest responses to drought with multiple seasonal metrics from Sentinel-2
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - 1569-8432. ; 128
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Tidskriftsartikel (refereegranskat)abstract
- Topography determines run-off direction, redistributes groundwater, and affects land surface solar radiation loads and the associated evaporative forcing, consequently, topography can modulate the impact of drought and heat waves on ecosystems. This topographic modulation effect, which typically occurs at the local scale, is often overlooked when assessing ecosystem drought responses using moderate-to-coarse spatial resolution satellite observations, such as the Moderate-resolution Imaging Spectroradiometer (MODIS) imagery. Sentinel-2 and Landsat imagery with finer resolution are suitable for monitoring changes at the local scale, however, studies relying on single vegetation metrics may fail to get a holistic picture of vegetation drought responses, particularly for forests that have complex physiological mechanisms. Here, we performed a comprehensive assessment of the topographic effects on coniferous forest responses to the severe 2018 drought in Scandinavia, using 6 vegetation seasonal metrics during 2017–2021 from the Sentienl-2 High-Resolution Vegetation Phenology and Productivity (HR-VPP) products. We found significant differences (p < 0.05) between sunny and shady aspects, between higher and lower elevations, and between steep and gentle slopes, regarding the maximum impact time, forest drought resistance, and resilience. Specifically, the sunny aspects and steep slopes were related to higher risks of delayed impacts and low resistance and resilience, and elevation and slope were more powerful in regulating the phenology shift and greening rate loss. We also identified different sensitivity in greenness and productivity to topographic effect and greater sensitivity of spring phenology to topographic differences as compared to autumn phenology. The study demonstrates vegetation drought responses represented by multiple seasonal metrics, reveals the prevalent topographic effects at the local scale, and quantifies the magnitudes of the effects with regional statistics.
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38. |
- Seaquist, Jonathan, et al.
(författare)
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Rapid estimation of photosynthetically active radiation over the West African Sahel using the Pathfinder Land Data Set
- 1999
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - 1569-8432. ; 1:3-4, s. 205-213
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Tidskriftsartikel (refereegranskat)abstract
- Photosynthetically Active Radiation (PAR) is important for assessing both the impact of changing land cover on climate, and for modelling productivity on a regional scale, as well as its potential in areas that are vulnerable to food shortfalls. A relatively simple method that generates spatially comprehensive and representative values of PAR at time scales of 10-days (dekads) or longer is described, tested and implemented over a portion of West Africa. With simple equations to describe the geographical and temporal variation of global radiation receipt at the top of the atmosphere, daily cloud flags from the NOAA/NASA AVHRR Pathfinder Land Data Set (PAL) are used in conjunction with an empirical formula developed by Angstrom and constants tailored to West African conditions to estimate surface receipt of global radiation there. Ground observations of PAR from the HAPEX Sahel experiment (at 13°66' N and 2°53' E from 1992) are used to parameterise the relative sunshine duration variable in the Angstrom relation so as to minimise errors between observed and modelled PAR. Results indicate that PAR may be estimated to within 20 percent of observed values for 28 out of 36 10-day summation periods over a year. End-of-year accumulated PAR is estimated to within 1.96 percent. Normalised root mean square errors (NRMSEs) and normalised mean absolute errors (NMAEs) of 15.69 percent and 12.46 percent, respectively, were obtained for 10-day sums, with values of 10.96 percent and 8.74 percent, respectively, for monthly sums. The spatial variability of end-of-year PAR for 1992 is in accordance with what was expected. Though more accurate methods exist for achieving this, the technique is merited for its ease of application, using an accessible data set, over areas where solar irradiation measurements are lacking.
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39. |
- Shami, Siavash, et al.
(författare)
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Assessments of ground subsidence along the railway in the Kashan plain, Iran, using Sentinel-1 data and NSBAS algorithm
- 2022
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 112
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Tidskriftsartikel (refereegranskat)abstract
- The 110-kilometer-long Qom-Kashan railway is one of the busiest lines in Iran, passing through the Kashan plain. The majority of Iran's plains have subsided in recent years as a result of uncontrolled groundwater extraction, and the Kashan plain is no exception. In this study, ground surface displacement in the Kashan plain region and its impact on the railway were investigated using New Small Baseline Subset (NSBAS) in up-down and east–west directions using descending and ascending Sentinel-1 data collected between 2015 and 2021. Our results indicate that the Kashan plain is subsiding more than 90 mm/year. The study of the local areas around the railway which passes through the study area revealed that the rate of vertical velocity in some locations reaches –23 mm/year, while the rate of east–west velocity is insignificant and is approximately ±2 mm/year. Additionally, a method for analyzing the railway's stability based on longitudinal profiles along the railway is presented. Our findings suggest that more than 60% of the railway line is subject to variable amounts of subsidence. Additionally, a region of approximately one kilometer of the railway has been classified as a risk zone due to relatively fast local deformation. After examining the effect of various factors, it was determined that uncontrolled groundwater extraction in agricultural areas contributed to the subsidence in this area. Our results show that the presented stability control approach in this study is highly reliable for creating hazard profiles for linear structures, such as railways.
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40. |
- Shami, Siavash, et al.
(författare)
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Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios.In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directions between 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predicted by the RFR model using SAR interferometry along two perpendicular profiles confirmed the model's precision.
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41. |
- Ståhl, Göran
(författare)
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Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory
- 2023
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - 1569-8432 .- 1872-826X. ; 119
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Tidskriftsartikel (refereegranskat)abstract
- Remote sensing (RS) has enhanced forest inventory with model-based inference, that is, a family of statistical procedures rigorously estimates the parameter of a variable of interest (VOI) for a spatial population, e.g., the mean or total of forest carbon for a study area. Upscaling in earth observation, alias to this estimation, aggregates VOI from a finer spatial resolution to a coarser one with reduced uncertainty, serving decision making for natural resource management at larger scales. However, conventional model-based estimation (CMB) confronts a major challenge: it only supports RS wall-to-wall data, meaning that remotely sensed data must be available in panorama and non-wall-to-wall but quality data such as lidar or even cloud-masked satellite imagery are not supported due to incomplete coverage, impeding precise upscaling with cutting-edge instruments or for large scale applications. Consequently, this study aims to develop and demonstrate the use and usefulness of RS nonwall-to-wall data for upscaling with Hierarchical model-based estimation (HMB) which incorporates a two-stage model for bridging RS non- and wall-to-wall data; and for optimizing cost-efficiency, to evaluate the effects of non-wall-to-wall sample size on upscaling precision. Three main conclusions are relevant: (1) the HMB is a variant of the CMB estimator through trading in the uncertainty of the second-stage model to enable estimation using RS non-wall-to-wall data; (2) a quality first-stage model is key to exerting the advantage of HMB relative to the CMB estimator; (3) the variance of the HMB estimator is dominated by the first-stage model variance component, indicating that increasing the sample size in the first-stage is effective for increasing the overall precision. Overall, the HMB estimator balances tradeoffs between cost, efficiency and flexibility when devising a model-based upscaling in earth observation.
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42. |
- Sun, Jia, et al.
(författare)
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Optimizing LUT-based inversion of leaf chlorophyll from hyperspectral lidar data : Role of cost functions and regulation strategies
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for an HSL system. In particular, the influences of i) different cost functions, ii) multiple best solutions (1–1000), iii) different LUT sizes (100–100000), and iv) spectral domains for leaf chlorophyll (Chl) retrieval were analyzed. An evaluation against an experimental dataset of rice leaves indicated that i) least-squares estimation (LSE) provided better estimates than seven alternative cost functions when more than 200 solutions were taken; ii) accuracy in leaf Chl retrieval increased up until 200 solutions where after it stabilized; iii) the impact of LUT size became insignificant after 1000; and iv) the red edge was the spectral domain that had the largest impact on the inversion performance. The optimal performance of leaf Chl estimation reached R2 of 0.58 and RMSE of 0.69 between the z-scores from retrieved and measured leaf Chl. The practical application of combining RTM with HSL data will facilitate the detection of leaf-level biochemistry and advance research on terrestrial carbon cycle modeling.
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43. |
- Tagesson, Torbern, et al.
(författare)
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High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992-2008
- 2012
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 18, s. 407-416
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Tidskriftsartikel (refereegranskat)abstract
- Arctic ecosystems play a key role in the terrestrial carbon cycle. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with field measurements of CO2 fluxes to investigate changes in gross primary production (GPP) for the peak growing seasons 1992-2008 in Rylekaerene, a wet tundra ecosystem in the Zackenberg valley, north-eastern Greenland. A method to incorporate controls on GPP through satellite data is the light use efficiency (LUE) model, here expressed as GPP = epsilon(peak) x PAR(in) x FAPAR(green_peak); where epsilon(peak) was peak growing season light use efficiency of the vegetation, PARin was incoming photosynthetically active radiation, and FAPAR(green_peak) was peak growing season fraction of PAR absorbed by the green vegetation. The Speak was measured for seven different high-Arctic plant communities in the field, and it was on average 1.63 g CO2 MJ(-1). We found a significant linear relationship between FAPARgreen_peak measured in the field and satellite-based NDVI. The linear regression was applied to peak growing season NDVI 1992-2008 and derived FAPAR(green_peak) was entered into the LUE-model. It was shown that when several empirical models are combined, propagation errors are introduced, which results in considerable model uncertainties. The LUE-model was evaluated against field-measured GPP and the model captured field-measured GPP well (RMSE was 192 mg CO2 m(-2) h(-1)). The model showed an increase in peak growing season GPP of 42 mg CO2 m(-2) h(-1) y(-1) in Rylekaerene 1992-2008. There was also a strong increase in air temperature (0.15 degrees C y(-1)), indicating that the GPP trend may have been climate driven. (C) 2012 Elsevier B.V. All rights reserved.
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44. |
- Thierfelder, Tomas, et al.
(författare)
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Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations
- 2022
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 108
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Tidskriftsartikel (refereegranskat)abstract
- Reliable and accurate soil moisture maps are needed to minimise the risk of soil disturbance during logging operations. Depth-to-water (DTW) maps extracted from digital elevation models have shown potential for identifying water flow paths and associated wet and moist areas, based on surface topography. We have examined whether DEMs from airborne LiDAR data with varying point density can improve performance of DTW maps in planning logging operations. Soil moisture content was estimated on eight sites after logging operations and compared to DTW maps created from DEMs with resolutions of 2 m, 1 m, and 0.5 m. Different threshold values for wet soil (1 m and 1.5 m depth to water) were also tested. The map performances, measured by accuracy (ACC) and Matthews Correlation Coefficient (MCC), changed slightly (79%, 81% and 82% and 0.33, 0.26 and 0.30 respectively) when DEM resolutions varied from 2 m to 1 m, and 0.5 m. The corresponding values when the DTW threshold value for wet/dry soil changed from 1 m to 1.5 m were 70%, 72%, 71% and 0.38, 0.41 and 0.39. LiDAR-based DEM resolutions of 1–2 m were found to be sufficient for extraction of DTW maps during planning of logging operations, when knowledge about soil hydrological features, associated wet and moist areas, and their connectivity is beneficial.
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45. |
- Vu, Tuong Thuy, et al.
(författare)
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Multi-scale solution for building extraction from LiDAR and image data
- 2009
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 11:4, s. 281-289
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a multi-scale solution based on mathematical morphology for extracting the building features from remotely sensed elevation and spectral data. Elevation data are used as the primary data to delineate the structural information and are firstly represented on a morphological scale-space. The behaviors of elevation clusters across the scale-space are the cues for feature extraction. As a result, a complex structure can be extracted as a multi-part object in which each part is represented on a scale depending on its size. The building footprint is represented by the boundary of the largest part. Other object attributes include the area, height or number of stories. The spectral data is used as an additional source to remove vegetation and possibly classify the building roof material. Finally, the results can be stored in a multi-scale database introduced in this paper. The proposed solution is demonstrated using the data derived from a Light Detection And Ranging (LiDAR) surveying flight over Tokyo, Japan. The results show a reasonable match with reference data and prove the capability of the proposed approach in accommodation of diverse building shapes. Higher density LiDAR is expected to produce better accuracy in extraction, and more spectral sources are necessary for further classification of building roof material. It is also recommended that parallel processing should be implemented to reduce the computation time.
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46. |
- Wästfelt, Anders, et al.
(författare)
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Local spatial context measurements used to explore the relationship between land cover and land use functions
- 2013
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X .- 0303-2434. ; 23, s. 234-244
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Tidskriftsartikel (refereegranskat)abstract
- Research making use of satellite data for land change science has developed in the last decades. However, analysis of land use has not developed with the same speed as development of new satellite sensors and available land cover data. Improvement of land use analysis is possible, but more advanced methods are needed which make it possible to link image data to analysis of land use functions. To make this linking possible, variable which affect farmer's long term decisions must be taken into account in analysis as well as the relative importance of the landscape itself. A GIS-based tool for the measurement of local spatial context in satellite data is presented in this paper and used to explore the relationship between land covers present in satellite data and land use represented in official databases. By the use of the developed tool, a land configuration image (LCI) over the Siljan area in northern Sweden was produced and used for analysis. The results are twofold. First, the produced LCI holds new information about variables that are relevant for the interpretation of land use. Second, the comparison with statistics of agricultural production shows that production in the study area varies depending on the relative land configuration. Villages consisting of relatively large-scale arable fields and less diverse landscape are less diverse in production than villages which consist of smaller-scale and more heterogonous landscapes. The result is especially relevant for land use studies and policymakers working on environmental and agricultural policies. We conclude that local spatial context is an endogenous variable in the relation between landscape configuration and agricultural land use.
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47. |
- Yadav, Ritu, et al.
(författare)
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Unsupervised flood detection on SAR time series using variational autoencoder
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 126
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we propose a novel unsupervised Change Detection (CD) model to detect flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is based on a spatiotemporal variational autoencoder, trained with reconstruction and contrastive learning techniques. The change maps are generated with a proposed novel algorithm that utilizes differences in latent feature distributions between pre-flood and post-flood data. The model is evaluated on nine different flood events by comparing the results with reference flood maps collected from the Copernicus Emergency Management Services (CEMS) and Sen1Floods11 dataset. We conducted a range of experiments and ablation studies to investigate the performance of our model. We compared the results with existing unsupervised models. The model achieved an average of 70% Intersection over Union (IoU) score which is at least 7% better than the IoU from existing unsupervised CD models. In the generalizability test, the proposed model outperformed supervised models ADS-Net (by 10% IoU) and DAUSAR (by 8% IoU), both trained on Sen1Floods11 and tested on CEMS sites. Our implementation will be available here https://github.com/RituYadav92/CLVAE-Unsupervised_Change_Detection_TimeSeriesSAR.
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48. |
- Zhang, Pengcheng, et al.
(författare)
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Geospatial learning for large-scale transport infrastructure depth prediction
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- Transportation infrastructure supports the smooth mobility of humans, commodities, and services. Pavement depth measures the quality of road infrastructure through representing the thickness of road surfaces, and influences various aspects of construction projects. However, accurately modeling and predicting pavement depth has been a critical challenge due to diverse and complex factors, such as weather dynamics, traffic patterns, maintenance interventions, and environmental fluctuations. This study develops a second-dimension spatial learning (SDSL) model that integrates geospatial models and machine learning for large-scale pavement depth prediction. SDSL models are implemented in pavement prediction for eight distinct regions in Western Australia, and they are validated using the observation of pavement depth through cross-validation. Results demonstrate that the proposed SDSL models can more accurately predict large-scale pavement depth than the existing first-dimension spatial learning (FDSL) models, with 17.3% to 37.6% increase of R2 values, 1.46% to 16.5% reduction of RMSE, 1.7% to 31.1% reduction of MAE and 21.0% reduction of prediction uncertainty. SDSL models enhance effective infrastructure management by accurately predicting pavement depth, essential for maintaining large-scale transportation infrastructure. The study significantly contributes to the efficient management of sustainable infrastructure assets, saving time and money. © 2024 The Authors
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49. |
- Zhang, Shuping, et al.
(författare)
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Mapping coastal upwelling in the Baltic Sea from 2002 to 2020 using remote sensing data
- 2022
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- Coastal upwelling (CU) is an elementary phenomenon in coastal waters. CU brings up deep, often cold, saline water rich of nutrients and carbon, and plays a vital role in local air-sea exchange of gases and heat, marine ecosystem maintenance, and ocean physical dynamics. In this study, regional CU in the Baltic Sea was mapped on the daily MODIS SST from 2002 to 2020, using a method modified developed by Lehmann et al. (2012). Based on the individual CU event detected, the spatiotemporal distribution of CU in the Baltic Sea was depicted, the CU-wind relationship and potential CU drivers in the Baltic Sea on different temporal scale were analyzed. The results found that: 1) The modified approach can effectively delineate the CUs formed by upwelled cold water; 2) The 19 zones delineated with frequent CU occurrences aligned well with previous study and the overall CU occurrence spatial heterogeneity was casted by the different directional relationship between the local coastline and wind; 3) The occurrences of the CU detected in this study showed strong seasonality and primarily driven by SST seasonality and then intensified by local wind in fall; 4) The interannual difference of CU occurrences were affected by heatwaves and its monthly timing. The CUs detected in this study have a high potential for facilitating investigations with respect to oceanic modeling, air-sea exchange of heat and greenhouse gases, and physical dynamics in the Baltic Sea.
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