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Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging – partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.
Fabian Löw; Klaus Stieglitz; Olga Diemar. Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan. Journal of Environmental Management 2021, 298, 113424 .
AMA StyleFabian Löw, Klaus Stieglitz, Olga Diemar. Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan. Journal of Environmental Management. 2021; 298 ():113424.
Chicago/Turabian StyleFabian Löw; Klaus Stieglitz; Olga Diemar. 2021. "Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan." Journal of Environmental Management 298, no. : 113424.
Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM), and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC).
Pengyu Hao; Fabian Löw; Chandrashekhar Biradar. Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia. Remote Sensing 2018, 10, 2057 .
AMA StylePengyu Hao, Fabian Löw, Chandrashekhar Biradar. Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia. Remote Sensing. 2018; 10 (12):2057.
Chicago/Turabian StylePengyu Hao; Fabian Löw; Chandrashekhar Biradar. 2018. "Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia." Remote Sensing 10, no. 12: 2057.
Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003–2016. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, “stratum-specific” classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate (p < 0.05) than a “global” classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation.
Fabian Löw; Alexander V. Prishchepov; François Waldner; Olena Dubovyk; Akmal Akramkhanov; Chandrashekhar Biradar; John P. A. Lamers. Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sensing 2018, 10, 159 .
AMA StyleFabian Löw, Alexander V. Prishchepov, François Waldner, Olena Dubovyk, Akmal Akramkhanov, Chandrashekhar Biradar, John P. A. Lamers. Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sensing. 2018; 10 (2):159.
Chicago/Turabian StyleFabian Löw; Alexander V. Prishchepov; François Waldner; Olena Dubovyk; Akmal Akramkhanov; Chandrashekhar Biradar; John P. A. Lamers. 2018. "Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series." Remote Sensing 10, no. 2: 159.
In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R2 = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.
Fabian Löw; Chandrashekhar Biradar; Olena Dubovyk; Elisabeth Fliemann; Akmal Akramkhanov; Alejandra Narvaez Vallejo; Francois Waldner. Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series. GIScience & Remote Sensing 2017, 55, 539 -567.
AMA StyleFabian Löw, Chandrashekhar Biradar, Olena Dubovyk, Elisabeth Fliemann, Akmal Akramkhanov, Alejandra Narvaez Vallejo, Francois Waldner. Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series. GIScience & Remote Sensing. 2017; 55 (4):539-567.
Chicago/Turabian StyleFabian Löw; Chandrashekhar Biradar; Olena Dubovyk; Elisabeth Fliemann; Akmal Akramkhanov; Alejandra Narvaez Vallejo; Francois Waldner. 2017. "Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series." GIScience & Remote Sensing 55, no. 4: 539-567.
The ground truth data sets required to train supervised classifiers are usually collected as to maximize the number of samples under time, budget and accessibility constraints. Yet, the performance of machine learning classifiers is, among other factors, sensitive to the class proportions of the training set. In this letter, the joint effect of the number of calibration samples and the class proportions on the accuracy was systematically quantified using two state-of-the-art machine learning classifiers (random forests and support vector machines). The analysis was applied in the context of binary cropland classification and focused on two contrasted agricultural landscapes. Results showed that the classifiers were more sensitive to class proportions than to sample size, though sample size had to reach 2,000 pixels before its effect leveled off. Optimal accuracies were obtained when the training class proportions were close to those actually observed on the ground. Then, synthetic minority over-sampling technique (SMOTE) was implemented to artificially regenerate the native class proportions in the training set. This resampling method led to an increase of the accuracy of up to 30%. These results have direct implications for (i) informing data collection strategies and (ii) optimizing classification accuracy. Though derived for cropland mapping, the recommendations are generic to the problem of binary classification.
François Waldner; Damien C Jacques; Fabian Löw. The impact of training class proportions on binary cropland classification. Remote Sensing Letters 2017, 8, 1122 -1131.
AMA StyleFrançois Waldner, Damien C Jacques, Fabian Löw. The impact of training class proportions on binary cropland classification. Remote Sensing Letters. 2017; 8 (12):1122-1131.
Chicago/Turabian StyleFrançois Waldner; Damien C Jacques; Fabian Löw. 2017. "The impact of training class proportions on binary cropland classification." Remote Sensing Letters 8, no. 12: 1122-1131.
"The Asian Migratory locust (Locusta migratoria migratoria L.) is a pest that continuously threatens crops\ud in the Amudarya River delta near the Aral Sea in Uzbekistan, Central Asia. Its development coincides with\ud the growing period of its main food plant, a tall reed grass (Phragmites australis), which represents the\ud predominant vegetation in the delta and which cover vast areas of the former Aral Sea, which is\ud desiccating since the 1960s. Current locust survey methods and control practices would tremendously\ud benefit from accurate and timely spatially explicit information on the potential locust habitat distribution.\ud To that aim, satellite observation from the MODIS Terra/Aqua satellites and in-situ observations\ud were combined to monitor potential locust habitats according to their corresponding risk of infestations\ud along the growing season. A Random Forest (RF) algorithm was applied for classifying time series of\ud MODIS enhanced vegetation index (EVI) from 2003 to 2014 at an 8-day interval. Based on an independent\ud ground truth data set, classification accuracies of reeds posing a medium or high risk of locust\ud infestation exceeded 89% on average. For the 12-year period covered in this study, an average of\ud 7504 km2 (28% of the observed area) was flagged as potential locust habitat and 5% represents a permanent\ud high risk of locust infestation. Results are instrumental for predicting potential locust outbreaks\ud and developing well-targeted management plans. The method offers positive perspectives for locust\ud management and treatment of infested sites because it is able to deliver risk maps in near real time, with\ud an accuracy of 80% in April-May which coincides with both locust hatching and the first control surveys.\ud Such maps could help in rapid decision-making regarding control interventions against the initial locust\ud congregations, and thus the efficiency of survey teams and the chemical treatments could be increased,\ud thus potentially reducing environmental pollution while avoiding areas where treatments are most likely\ud to cause environmental degradation.
Fabian Löw; Francois Waldner; Alexandre Latchininsky; Chandrashekhar Biradar; Maximilian Bolkart; René R. Colditz. Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index. Journal of Environmental Management 2016, 183, 562 -575.
AMA StyleFabian Löw, Francois Waldner, Alexandre Latchininsky, Chandrashekhar Biradar, Maximilian Bolkart, René R. Colditz. Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index. Journal of Environmental Management. 2016; 183 ():562-575.
Chicago/Turabian StyleFabian Löw; Francois Waldner; Alexandre Latchininsky; Chandrashekhar Biradar; Maximilian Bolkart; René R. Colditz. 2016. "Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index." Journal of Environmental Management 183, no. : 562-575.
This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman’s rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000–2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman’s rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region.
Christopher Conrad; Sarah Schönbrodt-Stitt; Fabian Löw; Denis Sorokin; Heiko Paeth. Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012. Remote Sensing 2016, 8, 630 .
AMA StyleChristopher Conrad, Sarah Schönbrodt-Stitt, Fabian Löw, Denis Sorokin, Heiko Paeth. Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012. Remote Sensing. 2016; 8 (8):630.
Chicago/Turabian StyleChristopher Conrad; Sarah Schönbrodt-Stitt; Fabian Löw; Denis Sorokin; Heiko Paeth. 2016. "Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012." Remote Sensing 8, no. 8: 630.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.