Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

Homer, Collin G.; Dewitz, Jon; Jin, Suming; Xian, George; Costello, Catherine; Danielson, Patrick; Gass, Leila; Funk, Michelle; Wickham, James D.; Stehman, Stephen V.; Auch, Roger F.; Riitters, Kurt H.

doi:10.1016/j.isprsjprs.2020.02.019

Cited by 523 publications

(382 citation statements)

References 46 publications

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“…These problems link to land cover/land use changes. While several mapping projects have been using coarse resolution satellite imagery for land cover change detection, they may not be effective for a complex and fragmented landscapes such as Central Vietnam [120,121], and our maps seem to be more suitable for land cover change analysis over Central Vietnam, and can provide the policy-makers and scientific associations with input data for the further discussion of environmental management, in particular water balance, sediment estimation, and food security. In addition, the maps can be a critical data for managing ecosystems and biodiversity such as for Global Forest Resources Assessment (FRA) of FAO.…”

Section: Potential Application and Future Workmentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

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Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

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Section: Potential Application and Future Workmentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

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“…In recent decades, many regions of the world have experienced an increase in frequency and intensity of wildfires, which may be possibly connected to changes in regional climate (Balshi et al, 2009;Barbero et al, 2015;Carvalho et al, 2008;Flannigan et al, 2009;Westerling et al, 2006;Westerling, 2016). More intense and more frequent wildfire activities not only heighten ecosystem vulnerability but also cause poor air quality (Jaffe et al, 2008;Pellegrini et al, 2017;Wang et al, 2018;Yue et al, 2015). Thus, it is imperative to understand how wildfires would respond to changes in environmental factors in a warming climate.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques

Wang

2020

Atmos. Chem. Phys.

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Abstract. Occurrences of devastating wildfires have been increasing in the United States for the past decades. While some environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between these factors and wildfires are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model consisting of multiple machine learning algorithms to predict 0.5∘×0.5∘ gridded monthly wildfire burned area over the south central United States during 2002–2015 and then use this model to identify the relative importance of the environmental drivers on the burned area for both the winter–spring and summer fire seasons of that region. The developed model alleviates the issue of unevenly distributed burned-area data, predicts burned grids with area under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves temporal correlations larger than 0.5 for more than 70 % of the grids and spatial correlations larger than 0.5 (p<0.01) for more than 60 % of the months. For the total burned area over the study domain, the model can explain 50 % and 79 % of the observed interannual variability for the winter–spring and summer fire season, respectively. Variable importance measures indicate that relative humidity (RH) anomalies and preceding months' drought severity are the two most important predictor variables controlling the spatial and temporal variation in gridded burned area for both fire seasons. The model represents the effect of climate variability by climate-anomaly variables, and these variables are found to contribute the most to the magnitude of the total burned area across the whole domain for both fire seasons. In addition, antecedent fuel amounts and conditions are found to outweigh the weather effects on the amount of total burned area in the winter–spring fire season, while fire weather is more important for the summer fire season likely due to relatively sufficient vegetation in this season.

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“…Derived model parameters describe topography, soil, land cover, vegetation, geology, and meteorology. We used the 30 m resolution elevation data from National Elevation Dataset (NED) (Gesch et al, ), USDA‐NRCS Soil Survey Geographic (SSURGO) soil data (Soil Survey Staff, Natural Resources Conservation Service, ), and National Land Cover Dataset (NLCD) land cover data (Homer et al, ). Meteorological forcings such as precipitation, air temperature, relative humidity, wind speed, and radiation were obtained from the North America Land Data Assimilation System Phase 2 (NLDAS‐2) data (Xia et al, ), which has a spatial and temporal resolution of 1/8 degree and an hour, respectively.…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal variations in the groundwater contributing areas of inland wetlands

Park

Wang

Kumar

2019

Hydrological Processes

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Water quality and groundwater dynamics in wetlands are strongly influenced by the spatiotemporal distribution of contaminant application, and variations and changes in climate, vegetation, and anthropogenic interventions in its neighborhood. For groundwater-fed wetlands, this relevant neighborhood at least extends to the groundwater contributing area (GCA) boundary. In spite of its importance, understanding of the nature of GCA dynamics vis-à-vis meteorological variations remains largely understudied. This work attempts to map GCA of inland forested wetlands. Following that, two specific questions are answered: (a) Is GCA extent and its variation different than that of the topographic contributing area (TCA)? and (b) Is the temporal dynamics of GCA for different wetlands, all of which are experiencing very similar climatological forcing, similar? Our results show that GCAs for wetlands vary temporally, are much different in extent and shape than the TCA, and on an average are larger than the TCA. Although wetlands in the studied watershed experienced similar meteorological forcings, their covariation with forcings varied markedly. Majority of the wetlands registered an increase in GCA during dry period, but for a few the GCA decreased. This highlights the role of additional physical controls, other than meteorological forcings, on temporal dynamics of GCA. Notably, wetlands with larger TCA are found to generally have larger average GCA as well, thus indicating the dominant role of topography in determining the relative size of average GCA over the landscape. Our results provide a refined picture of the spatiotemporal patterns of GCA dynamics and the controls on it. The information will help improve the prediction of wet period dynamics, recharge, and contamination risk of groundwater-fed wetlands.

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Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

Cited by 523 publications

References 46 publications

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques

Spatial and temporal variations in the groundwater contributing areas of inland wetlands

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