Land-cover change in the Caucasus Mountains since 1987 based on the topographic correction of multi-temporal Landsat composites

Buchner, Johanna; Yin, He; Frantz, David; Kuemmerle, Tobias; Askerov, Elshad; Bakuradze, Tamar; Bleyhl, Benjamin; Elizbarashvili, Nodar; Komarova, Anna; Lewińska, Katarzyna Ewa; Rizayeva, Afag; Sayadyan, Hovik; Tan, Bin; Tepanosyan, Garegin; Zazanashvili, Nugzar; Radeloff, Volker C.

doi:10.1016/j.rse.2020.111967

Cited by 58 publications

(44 citation statements)

References 78 publications

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“…All images were radiometrically standardized as outlined by Frantz et al (2016a) . The atmospheric correction included radiative transfer modelling ( Tanré et al, 1979 ), object-based Aerosol Optical Depth (AOD) estimation over dense dark vegetation ( Kaufman and Sendra, 1988 ) and water ( Royer et al, 1988 ), topographic correction using an enhanced C-correction as described in Buchner et al (2020) , adjacency effect correction ( Bach, 1995 ), and Nadir BRDF-adjustment using a global set of MODIS-derived BRDF kernel parameters ( Roy et al, 2016 ). Surface reflectance retrieval, as well as AOD estimation and cloud masking were assessed in the Atmospheric Correction and Cloud Masking Inter-comparison eXercises (ACIX/ACIX-II/CMIX, Doxani et al, 2018 ; ESA, 2019 ).…”

Section: Datamentioning

confidence: 99%

National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series

Frantz

Schug

Okujeni

et al. 2021

Remote Sensing of Environment

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136

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Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage.

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Section: Datamentioning

confidence: 99%

National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series

Frantz

Schug

Okujeni

et al. 2021

Remote Sensing of Environment

Self Cite

136

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“…We used several regional studies for the quantitative assessment of changes in landuse and landcover. Thus, cropland and forest areas for 1987-2015 were calculated based on Buchner et al (2020). While glacier areas for 1960-2014 were derived from Tielidze and Wheate's (2018) study.…”

Section: Landuse/landcover Change Assessmentmentioning

confidence: 99%

“…From these values, we calculated 95% confidence intervals on every SSL value (i.e., the difference between the 97.5% and 2.5% quantile of the 1000 simulated values). Buchner et al (2020) reported that the overall accuracy of the cropland change map is 75.7%, of the forest change map is 90.2 %. Therefore, uncertainties of the landcover change associated with measuring errors were simulated as a random number from a normal distribution with a mean of 1 and a standard deviation of 0.24 for cropland and 0.1 for a forest.…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

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How does the suspended sediment yield change in the North Caucasus during the Anthropocene?

Tsyplenkov

Golosov

Belyakova

2021

Preprint

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Quantifying and understanding catchment sediment yields is crucial both from a scientific and environmental management perspective. To deepen the understanding of land use impacts and climate change on sediment load, we explore mechanisms of the suspended sediment yield formation in the Northern Caucasus during the Anthropocene. We examine how sediment flux of various river basins with different land-use/landcover and glacier cover changes during the 1925-2018 period. Our analysis is based on observed mean annual suspended sediment discharges (SSD, kg·s−1) and annual fluxes (SSL, t·yr−1) from 33 Roshydromet gauging stations (Russia). SSL series have been analyzed to detect statistically significant changes during the 1925-2018 period. The occurrence of abrupt change points in SSD was investigated using cumulative sum (CUSUM) charts. We found that SSL has decreased by −1.81% per year on average at most gauges. However, the decline was not linear. Several transition years are expected in the region: increasing trends from the 1950s and decreasing trends from 1988-1994. Correlation analyses showed that variation in SSL trend values is mainly explained by gauging station altitude, differences in land use (i.e., the fraction of cropland), and catchment area. Nonetheless, more accurate quantifications of SSL trend values and more refined characterizations of the catchments regarding (historical) land use, soil types/lithology, weather conditions, and topography may reveal other tendencies.

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“…Patterns of land cover and land use (LCLU) change can be very complex, especially when investigated over long time periods and/or in areas with multiple and changing drivers of alteration [1][2][3][4][5][6]. Mapping and quantifying LCLU change is particularly difficult in topographically complex landscapes where inaccurate terrain correction can result in misalignment between analyzed datasets [7] and in heterogenous landscapes with many and varied transitions between thematic classes [8][9][10]. Long time series of moderate spatial resolution satellite imagery have been of great value in documenting and quantifying LCLU change during their operational lifespans [1,5,6].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps

et al. 2020

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Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land use (LCLU) information that could be used to expand the historic record when combined with data from moderate spatial resolution Earth observation missions. This is especially true for landscape disturbances that have a long and complex historic record, such as surface coal mining in the Appalachian region of the eastern United States. In this study, we investigate this specific mapping problem using modified UNet semantic segmentation deep learning (DL), which is based on convolutional neural networks (CNNs), and a large example dataset of historic surface mine disturbance extents from the USGS Geology, Geophysics, and Geochemistry Science Center (GGGSC). The primary objectives of this study are to (1) evaluate model generalization to new geographic extents and topographic maps and (2) to assess the impact of training sample size, or the number of manually interpreted topographic maps, on model performance. Using data from the state of Kentucky, our findings suggest that DL semantic segmentation can detect surface mine disturbance features from topographic maps with a high level of accuracy (Dice coefficient = 0.902) and relatively balanced omission and commission error rates (Precision = 0.891, Recall = 0.917). When the model is applied to new topographic maps in Ohio and Virginia to assess generalization, model performance decreases; however, performance is still strong (Ohio Dice coefficient = 0.837 and Virginia Dice coefficient = 0.763). Further, when reducing the number of topographic maps used to derive training image chips from 84 to 15, model performance was only slightly reduced, suggesting that models that generalize well to new data and geographic extents may not require a large training set. We suggest the incorporation of DL semantic segmentation methods into applied workflows to decrease manual digitizing labor requirements and call for additional research associated with applying semantic segmentation methods to alternative cartographic representations to supplement research focused on multispectral image analysis and classification.

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Land-cover change in the Caucasus Mountains since 1987 based on the topographic correction of multi-temporal Landsat composites

Cited by 58 publications

References 78 publications

National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series

National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series

How does the suspended sediment yield change in the North Caucasus during the Anthropocene?

Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps

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