| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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
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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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| 5. |
- 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
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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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| 6. |
- 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
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 1872-826X. ; 22, s. 86-98
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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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| 7. |
- 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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| 8. |
- 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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| 9. |
- 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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| 10. |
- 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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