| 1. |
- Ahmadi, Seyed Ali, et al.
(författare)
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Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach
- 2022
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Ingår i: European Journal of Remote Sensing. - : Informa UK Limited. - 2279-7254. ; 55:1, s. 520-539
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Tidskriftsartikel (refereegranskat)abstract
- Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the potential of the proposed graph-based method for individual tree detection in a complex forest region in Mazandaran, Iran. In particular, the graph-based method obtained Precision, Recall, and F1-score values of 0.64, 0.73, and 0.68, respectively. Furthermore, the intercomparison with the well-known and most used Local Maximum (LM) suggested the applicability of the proposed method. After point cloud generation, the proposed method was implemented entirely in Python using open-source packages, which increases its applicability for other scholars and managers. The source code of the proposed algorithm can be found at https://github.com/Seyed-Ali-Ahmadi/Graph-based_ITCD.
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| 2. |
- 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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| 3. |
- Ghaderi Bafti, Alireza, et al.
(författare)
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Automated actual evapotranspiration estimation : Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms
- 2024
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Ingår i: Atmospheric Research. - 0169-8095. ; 297
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Tidskriftsartikel (refereegranskat)abstract
- Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.
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| 4. |
- Ghorbanian, Arsalan, et al.
(författare)
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Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery
- 2022
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Ingår i: Water. - : MDPI AG. - 2073-4441. ; 14:2
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Tidskriftsartikel (refereegranskat)abstract
- Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.
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| 5. |
- Ghorbanian, Arsalan, et al.
(författare)
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Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:15
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Tidskriftsartikel (refereegranskat)abstract
- Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices
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| 6. |
- Ghorbanian, Arsalan, et al.
(författare)
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Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:13
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Tidskriftsartikel (refereegranskat)abstract
- Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.
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| 7. |
- Ghorbanian, Arsalan, et al.
(författare)
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Performance Evaluation of Six Gridded Precipitation Products throughout Iran Using Ground Observations over the Last Two Decades (2000–2020)
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:15
-
Tidskriftsartikel (refereegranskat)abstract
- Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.
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| 8. |
- Hamedi, Hamidreza, et al.
(författare)
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Measuring Lane-changing Trajectories by Employing Context-based Modified Dynamic Time Warping
- 2023
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Ingår i: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 216
-
Tidskriftsartikel (refereegranskat)abstract
- The spatial lane-changing (LC) behavior should be analyzed for the vehicles in transportation systems in order to identify the patterns of vehicles’ movements using the similarities detected in their lane-changing trajectories. The trajectory of an LC vehicle is a function of its context. The present paper utilized spatial footprints and external/internal contexts to contextualize a measure applicable to the similarities found between LC trajectories. While only the external context of the previous investigation was constrained to the surrounding vehicles on the road, this study has investigated the idea of the contribution of solar radiation to the lane-changing trajectory patterns. The similarities found between multi-dimensional trajectories were determined by offering context-based modified dynamic time warping (CMDTW), and the CMDTW technique with the Next Generation Simulation (NGSIM) dataset was carefully analyzed. The weighting framework used for each dimension made it possible to quantify the similarities between lane-changing trajectories using the AHP technique. The obtained results showed that not only the lane-changing procedure depends on the conditions of the lane changer, but this procedure also depends on the solar radiation and the surrounding vehicles offered as the external contexts. Additionally, by including different dimensions, both internal and external contexts, the similarity results of LC trajectories turn into a more realistic phenomenon. The potential of the context-based modified dynamic time warping algorithm in the detection of a trajectory with the maximum similarity is also enhanced. Furthermore, in order to determine the LC trajectories, we used the Fuzzy C-means (FCM) clustering technique. We utilized Cohen’s kappa for the evaluation of the Fuzzy C-means results, and since the calculated Kappa score exceeds 0.8, the clustering algorithm has an excellent performance. The results obtained by comparing the suggested technique with commonly used similarity measurement techniques indicated that the accuracy of the CMDTW technique outperforms other techniques in the detection of the lane-changing trajectory patterns. The suggested CMDTW method has therefore been effective in the identification of the patterns of lane-changing trajectory.
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| 9. |
- Hosseiny, Benyamin, et al.
(författare)
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Urban land use and land cover classification with interpretable machine learning – A case study using Sentinel-2 and auxiliary data
- 2022
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Ingår i: Remote Sensing Applications: Society and Environment. - : Elsevier BV. - 2352-9385. ; 28
-
Tidskriftsartikel (refereegranskat)abstract
- The European commission launch of the twin Sentinel-2 satellites provides new opportunities for land use and land cover (LULC) classification because of the readily availability of their data and their enhanced spatial, temporal and spectral resolutions. The rapid development of machine learning over the past decade led to data-driven models being at the forefront of high accuracy predictions of the physical world. However, the contribution of the driving variables behind these predictions cannot be explained beyond generalized metrics of overall performance. Here, we compared the performance of three shallow learners (support vector machines, random forest, and extreme gradient boosting) as well as two deep learners (a convolutional neural network and a residual network with 50 layers) in and around the city of Malmö in southern Sweden. Our complete analysis suite involved 141 input features, 85 scenarios, and 8 LULC classes. We explored the interpretability of the five learners using Shapley additive explanations to better understand feature importance at the level of individual LULC classes. The purpose of class-level feature importance was to identify the most parsimonious combination of features that could reasonably map a particular class and enhance overall map accuracy. We showed that not only do overall accuracies increase from shallow (mean = 84.64%) to deep learners (mean = 92.63%) but that the number of explanatory variables required to obtain maximum accuracy decreases along the same gradient. Furthermore, we demonstrated that class-level importance metrics can be successfully identified using Shapley additive explanations in both shallow and deep learners, which allows for a more detailed understanding of variable importance. We show that for certain LULC classes there is a convergence of variable importance across all the algorithms, which helps explain model predictions and aid the selection of more parsimonious models. The use of class-level feature importance metrics is still new in LULC classification, and this study provides important insight into the potential of more nuanced importance metrics.
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| 10. |
- Jamali, Sadegh
(författare)
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Analyzing Vegetation Trends with Sensor Data from Earth Observation Satellites
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Abstract This thesis aims to advance the analysis of nonlinear trends in time series of vegetation data from Earth observation satellite sensors. This is accomplished by developing fast, efficient methods suitable for large volumes of data. A set of methods, tools, and a framework are developed and verified using data from regions containing vegetation change hotspots. First, a polynomial-fitting scheme is tested and applied to annual Global Inventory Modeling and Mapping Studies (GIMMS)–Normalized Difference Vegetation Index (NDVI) observations for North Africa for the period 1982–2006. Changes in annual observations are divided between linear and nonlinear (cubic, quadratic, and concealed) trend behaviors. A concealed trend is a nonlinear change which does not result in a net change in the amount of vegetation over the period. Second, a systematic comparison between parametric and non-parametric techniques for analyzing trends in annual GIMMS-NDVI data is performed at fifteen sites (located in Africa, Spain, Italy, and Iraq) to compare how trend type and departure from normality assumptions affect each method’s accuracy in detecting long-term change. Third, a user-friendly program (Detecting Breakpoints and Estimating Segments in Trend, DBEST) has been developed which generalizes vegetation trends to main features, and characterizes vegetation trend changes. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST is tested and evaluated using both simulated NDVI data, and actual NDVI time series for Iraq for the period 1982-2006. Finally, a decision-making framework is presented to help analysts perform comprehensive analyses of trend/change in time series of satellite sensor data. The framework is based on a conceptual model of the main aspects of trend analyses, including identification of the research question, the required data, the appropriate variables, and selection of efficient analysis methods. To verify the framework, it is applied to four case studies (located in Burkina Faso, Spain, Sweden, and Senegal) using Moderate-resolution Imaging Spectroradiometer (MODIS)–NDVI data for the period 2000–2013. Each of the case studies successfully achieved its research aim(s), showing that the framework can achieve the main goal of the study which is to advance the analysis of nonlinear changes in vegetation. The methods developed in this thesis can help to contribute more accurate information about vegetation dynamics to the field of land cover change research.
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| 11. |
- Jamali, Sadegh, et al.
(författare)
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Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel
- 2014
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 141, s. 79-89
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Tidskriftsartikel (refereegranskat)abstract
- Over the last few decades, increasing rates of change in the structure and function of ecosystems have been brought about by human modification of land cover, of which a major component is vegetation. Metrics derived from linear regression models applied to high temporal resolution satellite data are commonly used to estimate rates of vegetation change. This approach implicitly assumes that vegetation changes gradually and linearly, which may not always be the case. In order to account for non-linear change in annual observations of vegetation from satellites, we test and apply a polynomial fitting-based scheme to annual GIMMS (Global Inventory Modeling and Mapping Studies)–NDVI (Normalized Difference Vegetation Index) observations for North Africa (including the Sahel) for the period 1982–2006. The scheme divides vegetation change into cubic, quadratic, linear, and “concealed” trend behaviors, the latter indicating that while no net change in vegetation amount has occurred over the period, the curve exhibits at least one minimum or/and maximum indicating that the vegetation has undergone change during the elapsed time period. Our results show that just over half the study area (51.9%) exhibit trends that are statistically significant, with a dominance of positive linear trends (22.2%) that are distributed in an east-west band across the Sahel, thus confirming previous studies. Non-linear trends occur much less frequently and are more widely scattered. Nevertheless, they tend to cluster within or on the outskirts of zones of linear trend, underscoring their importance for detecting anomalous change features. We also show that the ratio of linear vs. non-linear trends tends to be associated with different land cover types/land cover change estimates, many of which reflect biome-level controls on vegetation dynamics. However, more local drivers related to direct human impact, such as urbanization, cannot be ruled out. Our change detection approach retains the more complex signatures embedded in long-term time series by preserving details about change rates, therefore allowing for a more subtle interpretation of change trajectories on a case-by-case basis. The fitting method is entirely automated and does not require the judicious selection of thresholds. However, while polynomials can give a better fit, they like linear models are based on assumptions, and may sometimes lead to oversimplification or miss short-term variations. Our method can help to contribute more accurate information to one of the major goals of the burgeoning field of land change science, namely to observe and monitor land changes underway throughout the world.
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| 12. |
- Jamali, Sadegh, et al.
(författare)
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Comparing parametric and non-parametric approaches for estimating trends in multi-year NDVI
- 2012
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Konferensbidrag (refereegranskat)abstract
- The aim of this study is to systematically compare parametric and non-parametric techniques for analyzing trends in annual NDVI derived from NOAA AVHRR sensor in order to examine how trend type and departure from normality assumptions affect the accuracy of detecting long-term change. To generate annual data, the mean NDVI of a four-month long ‘green’ season was computed for fifteen sites (located in Africa, Spain, Italy, Sweden, and Iraq) from the GIMMS product for the periods 1982-2006. Trends in these time series were then estimated by Ordinary Least-Squares (OLS) regression (parametric) and the combined Mann-Kendall test with Theil-Sen slope estimator (non-parametric), and compared using slope value and statistical significance measures. We also estimated optimal polynomial model for the annual NDVI, by using Akaike Information Criterion (AIC), to determine the trend type at each site. Results indicate that slopes and their statistical significances obtained from the two approaches at sites with low degree polynomials (mostly linear) and steep monotonic (gradually increasing or decreasing) trends compare favourably with one another. At sites with weak linear slopes, the two approaches had similar results as well. Exceptions include sites with abrupt step-like changes resulting in departures from linearity and consequently high degree polynomials where the least-squares method outperformed the Mann-Kendall Theil-Sen method. In sum, we conclude that OLS is superior for detecting NDVI trends using annual data though further investigation using other techniques is recommended.
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| 13. |
- Jamali, Sadegh, et al.
(författare)
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Detecting changes in vegetation trends using time series segmentation
- 2015
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 156:January, s. 182-195
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Tidskriftsartikel (refereegranskat)abstract
- Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using one of three user-defined parameters: a generalisation-threshold parameter δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST was tested and evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, which included different types of changes. Evaluation results demonstrate that DBEST quickly and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time and magnitude. DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI image time series for Iraq for the period 1982–2006, and was able to detect and quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend detection, and is applicable to global change studies using time series of remotely sensed data sets.
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| 14. |
- Jamali, Sadegh, et al.
(författare)
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Examining the potential for early detection of spruce bark beetle attacks using multi-temporal Sentinel-2 and harvester data
- 2023
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Ingår i: ISPRS Journal of Photogrammetry and Remote Sensing. - 0924-2716. ; 205, s. 352-366
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Tidskriftsartikel (refereegranskat)abstract
- Forests are invaluable terrestrial ecosystems with considerable economic, ecological, and environmental benefits. Bark beetles have been recognized as one of the major causes of forest disturbance, and climate change can exacerbate their impact, leading to more tree mortality. Early detection of bark beetle attacks is vital to reduce forest loss and devastating consequences. This study examines the potential for early detection of European spruce bark beetle (Ips typographus L.) attacks in southeastern Sweden using comprehensive harvester data and time series of Sentinel-2 images, 2015–2021. Specifically, it aims at 1) determining the most pronounced wavelength bands and vegetation indices (VIs) of Sentinel-2 for early detection, 2) determining the number of attacked trees in a Sentinel-2 pixel required to enable detection, 3) testing three change detection approaches, Detecting Breakpoints and Estimating Segments in Trend (DBEST), Mean-Level-Shift (MLS), and Cumulative Sum (CUSUM) to investigate the potential for early detection of bark beetle attacks. The greatest separation in reflectance between healthy and attacked pixels, from first swarming peak (May 2018) till harvesting (April 2019), was observed in the SWIR1 (0.018) and SWIR2 (0.011) bands followed by red-edge (0.008), red (0.007), NIR (0.005), and the green band (0.004). The blue band showed the least separation (0.003). All VIs showed a change in their base level after the swarming and this was more prominent for NDRS with an increase of 0.14, followed by NDWI (-0.13), CCI (-0.11) and NDVI (-0.09), all with decreasing values. The observed responses of VIs in relation to the number of attacked trees in a Sentinel-2 pixel increased gradually for pixels having one to ten infested trees, with the strongest response observed for pixels with 9 to 14 attacked trees. Pixels including more than 14 attacked trees did not show any further substantial change in VIs. DBEST, on average, indicated that the infestation impact on VIs is detectable one month after the swarming peak with a 15–31 days precision. MLS and CUSUM with up to two months' accuracies were ranked next. NDRS and CCI showed superior detection performance compared to NDVI and NDWI. The detection was based on smoothed time series of Sentinel-2 data to reduce the influence of noise and missing data and cannot be directly applied to a near real-time detection method.
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| 15. |
- Jamali, Sadegh, et al.
(författare)
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Global-Scale Patterns and Trends in Tropospheric NO2 Concentrations, 2005–2018
- 2020
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 12:21
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Tidskriftsartikel (refereegranskat)abstract
- Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO2 concentrations for the time period 2005–2018. The OMI NO2 concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO2. The global average trend indicated slightly increasing NO2 concentrations (0.004 × 1015 molecules cm−2 y−1) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO2 concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health.
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| 16. |
- Jamali, Sadegh, et al.
(författare)
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Investigating temporal relationships between rainfall, soil moisture and MODIS-derived NDVI and EVI for six sites in Africa
- 2011
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Konferensbidrag (refereegranskat)abstract
- This study investigates temporal relationships between vegetation growth, rainfall, and soil moisture for six sites located in sub-Saharan and southern Africa for the period 2005-2009. Specifically, seasonal components of time series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectroradiometer (MODIS) and half-hourly in-situ rainfall and soil moisture data at different depths (5-200 cm) during the growing season were used in a lagged correlation analysis in order to understand how vegetation growth responds to rainfall and soil moisture across different sites. Results indicate that both vegetation indices are strongly related to soil moisture (EVI slightly stronger than NDVI) for the upper 1 m reaching maximum correlations when they lag soil moisture by 0-28 days. They respond to rainfall with a 24-32 day lag at the sub-Saharan sites, EVI slightly earlier than NDVI, but their response at the southern hemisphere sites is complex.
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| 17. |
- Jamali, Sadegh, et al.
(författare)
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Satellite-Observed Spatial and Temporal Sea Surface Temperature Trends of the Baltic Sea between 1982 and 2021
- 2023
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- The Baltic Sea is one of the fastest-warming marginal seas globally, and its temperature rise has adversely affected its physical and biochemical characteristics. In this study, forty years (1982–2021) of sea surface temperature (SST) data from the advanced very high resolution radiometer (AVHRR) were used to investigate spatial and temporal SST variability of the Baltic Sea. To this end, annual maximum and minimum SST stacked series, i.e., time series of stacked layers of satellite data, were generated using high-quality observations acquired at night and were fed to an automatic algorithm to detect linear and non-linear trend patterns. The linear trend pattern was the dominant trend type in both stacked series, while more pixels with non-linear trend patterns were detected when using the annual minimum SST. However, both stacked series showed increases in SST across the Baltic Sea. Annual maximum SST increased by an average of 0.062 ± 0.041 °C per year between 1982 and 2021, while annual minimum SST increased by an average of 0.035 ± 0.017 °C per year over the same period. Averaging annual maximum and minimum trends produces a spatial average of 0.048 ± 0.022 °C rise in SST per year over the last four decades.
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| 18. |
- Kazemi, Hamideh, et al.
(författare)
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Climate vs. Human Impact : Quantitative and Qualitative Assessment of Streamflow Variation
- 2021
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Ingår i: Water. - : MDPI AG. - 2073-4441. ; 13:17
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.
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| 19. |
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| 20. |
- Kazemzadeh, Majid, et al.
(författare)
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Detecting the Greatest Changes in Global Satellite-Based Precipitation Observations
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:21
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the analysis of abrupt and non-abrupt changes in precipitation has received much attention due to the importance of climate change-related issues (e.g., extreme climate events). In this study, we used a novel segmentation algorithm, DBEST (Detecting Breakpoints and Estimating Segments in Trend), to analyze the greatest changes in precipitation using a monthly pixel-based satellite precipitation dataset (TRMM 3B43) at three different scales: (i) global, (ii) continental, and (iii) climate zone, during the 1998–2019 period. We found significant breakpoints, 14.1%, both in the form of abrupt and non-abrupt changes, in the global scale precipitation at the 0.05 significance level. Most of the abrupt changes were observed near the Equator in the Pacific Ocean and Asian continent, relative to the rest of the globe. Most detected breakpoints occurred during the 1998–1999 and 2009–2011 periods on the global scale. The average precipitation change for the detected breakpoint was ±100 mm, with some regions reaching ±3000 mm. For instance, most portions of northern Africa and Asia experienced major changes of approximately +100 mm. In contrast, most of the South Pacific and South Atlantic Ocean experienced changes of −100 mm during the studied period. Our findings indicated that the larger areas of Africa (23.9%), Asia (22.9%), and Australia (15.4%) experienced significant precipitation breakpoints compared to North America (11.6%), South America (9.3%), Europe (8.3%), and Oceania (9.6%). Furthermore, we found that the majority of detected significant breakpoints occurred in the arid (31.6%) and polar (24.1%) climate zones, while the least significant breakpoints were found for snow-covered (11.5%), equatorial (7.5%), and warm temperate (7.7%) climate zones. Positive breakpoints’ temporal coverage in the arid (54.0%) and equatorial (51.9%) climates were more than those in other climates zones. Here, the findings indicated that large areas of Africa and Asia experienced significant changes in precipitation (−250 to +250 mm). Compared to the average state (trend during a specific period), the greatest changes in precipitation were more abrupt and unpredictable, which might impose a severe threat to the ecology, environment, and natural resources.
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| 21. |
- Kazemzadeh, Majid, et al.
(författare)
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Four Decades of Air Temperature Data over Iran Reveal Linear and Nonlinear Warming
- 2022
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 36:3, s. 462-477
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Tidskriftsartikel (refereegranskat)abstract
- Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there is considerable research on air temperature change across the globe, most of it has been on linear trends and time series analysis of nonlinear trends has not received enough attention. Here, we analyze spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax) and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-year time period (1978–2017). We applied a polynomial fitting scheme (Polytrend) to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The highest magnitude of increasing trends were observed in the annual Tmin (0.47 °C per decade) and the lowest magnitude was for the annual Tmax (0.4°C per decade). Across the country, increasing trends (x̄ = 37.2%) had higher spatial coverage than the decreasing trends (x̄ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) were mainly observed in humid climate zone while warming trends in Tmax were in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend was predominant in all Tmean (56.7%), Tmax (67.8%) and Tmin (71.2%) and for both monthly and annual datasets. Further, the linear trends had the highest warming rate in annual Tmin (0.83°C per decade) and Tmean (0.46°C per decade) whereas the nonlinear trends had the highest warming rate in annual Tmax (0.52°C per decade). The linear trend type was predominant in humid climate zones whereas the nonlinear trends (quadratic and cubic) were mainly observed in the arid climate zones.
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| 22. |
- Kazemzadeh, Majid, et al.
(författare)
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Linear and nonlinear trend analyses in global satellite‐based precipitation, 1998‐2017
- 2021
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Ingår i: Earth's Future. - 2328-4277. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Precipitation varies spatiotemporally in amount, intensity, and frequency. Although, much research has been conducted on analyzing precipitation patterns and variation at the global scale, trend types have still not received much attention. This study developed a new polynomial‐based model for detecting non‐linear and linear trends in a satellite precipitation product (TRMM 3B43) for the 1998‐2017 period at a near‐global scale. We used an automated trend classification method that detects significant trends and classifies them into linear and non‐linear (cubic, quadratic, and concealed) trend types in satellite‐based precipitation at near‐global, continental, and climate zone scales. We found that 12.3% of pixel‐based precipitation time series across the globe have significant trend at 0.05 significance level (50% positive and 50% negative trends). In all continents except Asia, decreasing trends were found to cover larger areas than corresponding increasing trends. Regarding climate zone and precipitation trend change, our results indicate that a linear trend is dominant in the warm temperate (77.7%) and equatorial climates (80.4%) while the least linear change was detected in the polar climate (68.9%). The combined results of continental and climate zone scales indicate significant increasing trends in Asia and arid climate over the last 20 years. Furthermore, positive trends were found to be more significant at the continental scale, particularly, in Asia relative to the climate zone scale. Linear change in precipitation (80%) was the most dominant trend observed as opposed to non‐linear (quadratic (11%) and cubic (9%)) trend types at the global scale.
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| 23. |
- Kazemzadeh, Majid, et al.
(författare)
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Natural and anthropogenic forcings lead to contrasting vegetation response in long-term vs. short-term timeframes
- 2021
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Ingår i: Journal of Environmental Management. - : Elsevier BV. - 0301-4797. ; 286
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Tidskriftsartikel (refereegranskat)abstract
- Understanding vegetation response to natural and anthropogenic forcings is vital for managing watersheds as natural ecosystems. We used a novel integrated framework to separate the impacts of natural factors (e.g. drought, precipitation and temperature) from those of anthropogenic factors (e.g. human activity) on vegetation cover change at the watershed scale. We also integrated several datasets including satellite remote sensing and in-situ measurements for a twenty-year time period (2000–2019). Our results show that despite no significant trend being observed in temperature and precipitation, vegetation indices expressed an increasing trend at both the control and treated watersheds. The vegetation cover was not significantly affected by the natural factors whereas the watershed management practice (as a human activity) had significant impacts on vegetation change in the long-term. Further, the vegetation cover long-term response to watershed management practice was mainly linear. We also found that the vegetation indices values in the 2011–2019 period (as the treated period in treated watershed) were significantly higher than those in the 2000–2010 period. In the short-term, however, the drought condition and decreased precipitation (as natural factors) explained the majority of the change in vegetation cover. For example, the majority of the breakpoints occurred in 2008, and it was related to a widespread extreme drought in the area. The watershed management practice as a human activity along with extreme climatic events could explain a large part of the vegetation changes observed in the treated and control watersheds.
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| 24. |
- Mohseni, Farzane, et al.
(författare)
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Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:18
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Tidskriftsartikel (refereegranskat)abstract
- SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.
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| 25. |
- Mohseni, Farzane, et al.
(författare)
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Global Soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm
- 2023
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Ingår i: Global and Planetary Change. - 1872-6364. ; 231
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Tidskriftsartikel (refereegranskat)abstract
- The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends.
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| 26. |
- Mohseni, Farzane, et al.
(författare)
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Ocean water quality monitoring using remote sensing techniques: A review
- 2022
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Ingår i: Marine Environmental Research. - : Elsevier BV. - 0141-1136. ; 180
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Tidskriftsartikel (refereegranskat)abstract
- Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
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| 27. |
- Müller, Mitro, et al.
(författare)
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Features predisposing forest to bark beetle outbreaks and their dynamics during drought
- 2022
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Ingår i: Forest Ecology and Management. - : Elsevier BV. - 1872-7042 .- 0378-1127. ; 523
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Tidskriftsartikel (refereegranskat)abstract
- Climate change is estimated to increase the risk of the bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for bark beetles may expand, and an increase in the frequency and intensity of droughts can promote drought stress on host trees. Drought affects tree vigor and in unison with environmental features it influences the local predisposition risk of forest stands to bark beetle attacks. We aimed to study how various environmental features influence the risk of bark beetle attacks during a drought year and the following years with more normal weather conditions but with higher bark beetle populations. We included features representing local forest stand attributes, topography, soil type and wetness, the proximity of clear-cuts and previous bark beetle attacks, and a machine learning algorithm (random forest) was applied to study the variation of predisposition risk across a 48,600 km2 study area in SE Sweden.Forest stands with increased risk of bark beetle attack were distinguished with high accuracy both during drought and in normal weather conditions. The results show that during both study periods, spruce and mixed coniferous forests had elevated risk of attack, while forests with a mix of deciduous and coniferous trees had a lower risk. Forests with high average canopy height were strongly predisposed to bark beetle attacks. However, during the drought year risk was more similar between stands with lower and higher canopy height, suggesting that during drought periods younger trees can be predisposed to bark beetle attacks. The importance of soil moisture and position within the local landscape were highlighted as important features during the drought year.Identifying areas with increased risk, supported by information on how environmental features control the predisposition risk during drought, could aid adaptation strategies and forest management intervention efforts. We conclude that geospatial data and machine learning have the potential to further support the digitalization of the forest industry, facilitating development of methods capable to quantify importance and dynamics of environmental features controlling the risk in local context. Corresponding methods could help to direct management actions more effectively and offer information for decision-making in changing climate.
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| 28. |
- Najafzadeh, Faezeh, et al.
(författare)
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Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:21
-
Tidskriftsartikel (refereegranskat)abstract
- Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.
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| 29. |
- Palmqvist, Carl-William, et al.
(författare)
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Satellite Monitoring of Railways using Interferometric Synthetic Aperture Radar (InSAR)
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- There is over 15,600 km of track in the Swedish railroad network. This network is vital for the transportation of people and goods across the country. It is important that this network is monitored and maintained to ensure good function and safety. A tool for monitoring and measuring ground deformation over a large area remotely with high frequency and accuracy was developed in recent decades. This tool is known as Interferometric Synthetic Aperture Radar (InSAR), and is used by researchers, geo-technicians, and engineers.The purpose of this study has been to evaluate the use and feasibility of the InSAR technique for track condition monitoring and compare it to conventional track condition monitoring techniques. Malmbanan, which is primarily used to transport iron-ore from mines in Sweden to the ports of Luleå, Sweden and Narvik, Norway, is used as a case study for this project; specifically, the section between Kiruna and Riksgränsen. Coordinate matching of measurements from the provided Persistent Scatterer Interferometry (PSI) InSAR data and Optram data from survey trains were performed. Then measured changes over different time spans within the two systems were overlapped and classified with different thresholds to see if there is correlation between the two systems. An extensive literature review was also conducted in order to gain an understanding of InSAR technologies and uses.The literature review showed that there is a large potential and a quickly growing number of applications of InSAR to monitor railways and other types of infrastructure, and that the tools and algorithms for this are being improved. The case study, on the other hand, shows that it can be difficult to directly compare measurement series from different tools, each working on different resolutions in terms of both time and space. InSAR is thus not about to replace techniques such as those behind Optram (using measurement trains). Instead, the approaches offer complementary perspectives, each highlighting different types of issues.We find that InSAR offers a good way to identify locations with settlements or other types of ground motions. Especially transition zones between settlements and more stable ground can be challenging from a maintenance point of view and can clearly be identified and monitored using InSAR. With the rollout of national InSAR-data, and the large increase in data accessibility, we see a considerable potential for future studies that apply the technique to the railway area.
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| 30. |
- Sahraei, Roya, et al.
(författare)
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Mangrove plantation suitability mapping by integrating multi criteria decision making geospatial approach and remote sensing data
- 2023
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Ingår i: Geo-Spatial Information Science. - : Informa UK Limited. - 1009-5020 .- 1993-5153.
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Tidskriftsartikel (refereegranskat)abstract
- Mangroves are woody plant communities that appear in tropical and subtropical regions, mainly in intertidal zones along the coastlines. Despite their considerable benefits to humans and the surrounding environment, their existence is threatened by anthropogenic activities and natural drivers. Accordingly, it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations. These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings. Therefore, identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate. This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan, Iran. To this end, we applied a hybrid Fuzzy-DEMATEL-ANP (FDANP) model as a Multi-Criteria Decision Making (MCDM) approach to determine the relative importance of different criteria, combined with geospatial and remote sensing data. In this regard, ten relevant sources of environmental criteria, including meteorological, topographical, and geomorphological, were used in the modeling. The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification. Based on the final results, 6.10% and 20.80% of the study area were classified as very-high suitable and very-low suitable areas. The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning. Moreover, the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
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| 31. |
- York, Ashley V., et al.
(författare)
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Change Points Detected in Decadal and Seasonal Trends of Outlet Glacier Terminus Positions across West Greenland
- 2020
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 12:21
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Tidskriftsartikel (refereegranskat)abstract
- We investigated the change in terminus position between 1985 and 2015 of 17 marine-terminating glaciers that drain into Disko and Uummannaq Bays, West Greenland, by manually digitizing over 5000 individual frontal positions from over 1200 Landsat images. We find that 15 of 17 glacier termini retreated over the study period, with ~80% of this retreat occurring since 2000. Increased frequency of Landsat observations since 2000 allowed for further investigation of the seasonal variability in terminus position. We identified 10 actively retreating glaciers based on a significant positive relationship between glaciers with cumulative retreat >300 m since 2000 and their average annual amplitude (seasonal range) in terminus position. Finally, using the Detecting Breakpoints and Estimating Segments in Trend (DBEST) program, we investigated whether the 2000–2015 trends in terminus position were explained by the occurrence of change points (significant trend transitions). Based on the change point analysis, we found that nine of 10 glaciers identified as actively retreating also underwent two or three periods of change, during which their terminus positions were characterized by increases in cumulative retreat. Previous literature suggests potential relationships between our identified change dates with anomalous ocean conditions, such as low sea ice concentration and high sea surface temperatures, and our change durations with individual fjord geometry.
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| 32. |
- Zhang, Wenxin, et al.
(författare)
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Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697.
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Tidskriftsartikel (refereegranskat)abstract
- Southwest China has been the largest terrestrial carbon sink in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.
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