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

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2020, Volume and Issue: 162, P. 184 - 199

Published: March 3, 2020

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous bare ground fractional percentages. release of NLCD provides important new information change patterns across CONUS from 2001 to 2016. For seven epochs were concurrently generated years 2001, 2004, 2006, 2008, 2011, 2013, Products reveal that cover is significant most classes time periods. was validated using existing reference data legacy 2011 accuracy assessment, applied epoch line. overall accuracies 82% 83%, respectively, (standard error (SE) 0.5%), demonstrating a small but increase in accuracy. Between 2016, landscape experienced change, with almost 8% having at least once during this period. Nearly 50% involves forest, driven by agents harvest, fire, disease pests resulted an forest decline, including increasing fragmentation loss interior forest. Agricultural represented 15.9% total agricultural spatial extent showing only slight 4778 km2, however there substantial decline (7.94%) pasture/hay time, transitioning mostly cultivated crop. Water wetland comprised 15.2% represent highly dynamic epoch, heavily influenced precipitation. Grass shrub comprise 14.5% resulting fire. Developed persistent permanent adding 29,000 km2 15 (5.6% change), southern states exhibiting expansion much faster than northern states. Temporal rates developed increased 2001-2006 twice rate 2011-2016, reflecting slowdown economic activity. Future plans include monitoring frequency, reducing latency between satellite imaging delivery, improving expanding variety available integrated database.

Language: Английский

---

Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Agricultural and Forest Meteorology, Journal Year: 2021, Volume and Issue: 301-302, P. 108350 - 108350

Published: Feb. 16, 2021

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models remote-sensing products. This study addresses one major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual sites reflect model- or satellite-based grid cells? We evaluate footprints—the temporally dynamic source areas that contribute fluxes—and representativeness these footprints for target within 250–3000 m radii around towers) often in flux-data synthesis modeling studies. examine land-cover composition vegetation characteristics, represented here by Enhanced Vegetation Index (EVI), across 214 sites, potential biases as a consequence footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary through time ranging four orders magnitude from 103 107 m2 due measurement heights, underlying vegetation- ground-surface wind directions, turbulent state atmosphere. Few located truly homogeneous landscape. Thus, common integration approaches use fixed-extent area introduce on order 4%–20% EVI 6%–20% dominant land cover percentage. These site-specific functions extents, land-surface characteristics. advocate need be awareness, especially research applications against data products explicit information. propose simple index based our evaluations can guide identify site-periods suitable specific provide general guidance use.

Language: Английский

---

Flood exposure and social vulnerability in the United States

Natural Hazards, Journal Year: 2021, Volume and Issue: 106(1), P. 435 - 457

Published: Jan. 4, 2021

Abstract Human exposure to floods continues increase, driven by changes in hydrology and land use. Adverse impacts amplify for socially vulnerable populations, who disproportionately inhabit flood-prone areas. This study explores the geography of flood social vulnerability conterminous United States based on spatial analysis fluvial pluvial extent, cover, vulnerability. Using bivariate Local Indicators Spatial Association, we map hotspots where high converge identify dominant indicators within these places. The hotspots, home approximately 19 million people, occur predominantly rural areas across US South. Mobile homes racial minorities are most overrepresented compared elsewhere. results priority locations interventions can mitigate both physical aspects variables that distinguish clusters used develop an indicator set exposure. Understanding is exposed where, be tailor mitigation strategies target those need.

Language: Английский

---

Cropland expansion in the United States produces marginal yields at high costs to wildlife

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Sept. 9, 2020

Abstract Recent expansion of croplands in the United States has caused widespread conversion grasslands and other ecosystems with largely unknown consequences for agricultural production environment. Here we assess annual land use change 2008–16 its impacts on crop yields wildlife habitat. We find that have expanded at a rate over one million acres per year, 69.5% new cropland areas produced below national average, mean yield deficit 6.5%. Observed infringed upon high-quality habitat that, relative to unconverted land, had provided three times higher milkweed stem densities Monarch butterfly Midwest summer breeding range 37% more nesting opportunities acre waterfowl Prairie Pothole Region Northern Great Plains. Our findings demonstrate pervasive pattern encroachment into are increasingly marginal production, but highly significant wildlife, suggest such tradeoffs may be further amplified by future expansion.

Language: Английский

---

Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 264, P. 112571 - 112571

Published: June 30, 2021

With the advent of next generation space-based laser altimeters, ICESat-2 and GEDI, we are entering an exciting era active remote sensing forests that offers unprecedented opportunities for observation forest structure. Consistent comparisons accuracy terrain canopy height retrievals these two missions essential continued improvement further application. Because time interval between spaceborne products validation data may introduce additional errors, validate newly released GEDI L2A product (version 2) ATL08 4) using high-resolution, locally calibrated airborne lidar acquired in same year (2019) as reference datasets. In addition, our study area contains 40 sites located U.S. mainland, Alaska, Hawaii encompass a variety eco-climatic zones vegetation cover types; thus, it avoids uncertainties associated with small sample sizes restricted spatial coverage. The results show yield reasonable estimates height, root mean squared errors (RMSEs) 2.24 4.03 m mid low latitudes, respectively, 0.98 high latitudes (ICESat-2 only). outperforms across board retrieval, although they both have better than existing SRTM GMTED DEM products. Analyses error factors suggest steep slopes (>30°) present greatest challenge ICESat-2; tall (>20 m) dense canopies (>90%) ecosystems also reduce estimates. When used use only strong/power beam at night is recommended, overall RMSEs decrease from 7.21 5.02 to 3.93 3.56 m, compared all regardless daytime strength. has larger potential bias almost types conditions. tends overestimate dwarf shrublands underestimate forest, there gradual downward shift distribution residuals increasing height. Overall, photon counting technology full waveform each represent state art altimeters retrieval. Combined, can take advantage unique strengths instrument.

Language: Английский

---

Widespread woody plant use of water stored in bedrock

Nature, Journal Year: 2021, Volume and Issue: 597(7875), P. 225 - 229

Published: Sept. 8, 2021

Language: Английский

---

Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(21), P. 3523 - 3523

Published: Oct. 27, 2020

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and management applications. There is, therefore, a clear need continuous reliable monitoring of changes. The growing availability high resolution, regularly collected remote sensing data can support increasing number applications that require spatial resolution products are frequently updated (e.g., annually). However, large-scale operational mapping requires highly-automated processing workflow, which currently lacking. To address this issue, we developed methodology automated classification multi-temporal Sentinel-2 imagery. method uses random forest classifier existing cover/use databases as source training samples. In order to demonstrate its operability, was implemented on large part European continent, with CORINE Land Cover High-Resolution Layers datasets. A map year 2017 produced, composed 13 classes. An accuracy assessment, based nearly 52,000 samples, revealed thematic overall (86.1%) continental scale, average 86.5% at country level. Only low-frequency classes obtained lower accuracies recommend their should be improved in future. Additional modifications legend, notably fusion thematically spectrally similar vegetation classes, increased 89.0%, resulted ten, general crucial aspect presented approach it embraces all most important elements Earth observation processing, enabling accurate detailed (10 m resolution) no manual user involvement. demonstrates possibility frequent repetitive production maps.

Language: Английский

---

Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Information Fusion, Journal Year: 2022, Volume and Issue: 86-87, P. 44 - 75

Published: June 25, 2022

Language: Английский

---

Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 268, P. 112780 - 112780

Published: Nov. 8, 2021

Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well providing core information support science activities. The ability generate maps has benefited free open access imagery, increased storage computational power. accuracy of the directly linked calibration (or training) used, predictors ancillary included in classification model, implementation classification, among other factors (e.g., algorithm, heterogeneity). Various means for improving can be implemented, including using independent datasets further refine training prior mapping. Opportunities also arise a profusion possible pre-existing products (static time series) forest inventory through observation airborne spaceborne lidar observations. In this research, 650 Mha forested ecosystems Canada, we explored approaches data, integrate novel predictors, optimize classifier implementation. We refined measures vertical structure, integrated spatial (via distance-to metrics) model implemented regionalized approach optimizing selection model-building ensure local relevance capture regional variability conditions. found that additional vetting involved removal 44.7% erroneous samples (e.g. treed vegetation without structure) pool. Nationally, distance ephemeral waterbodies was key predictor cover, while importance permanent water bodies varied on basis. Regionalization ensured models used locally relevant descriptors resulted improved outcomes (overall accuracy: 77.9% ± 1.4%) compared generalized, national (70.3% 2.5%). methodological developments presented herein are portable projects, monitoring programs, sources. increasing availability mapping, non-image aiding with development (from complementary layers) provide new opportunities improve automate procedures.

Language: Английский

---

A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape

Land, Journal Year: 2021, Volume and Issue: 10(9), P. 994 - 994

Published: Sept. 21, 2021

Land is a natural resource that humans have utilized for life and various activities. use/land cover change (LULCC) has been of great concern to many countries over the years. Some main reasons behind LULCC are rapid population growth, migration, conversion rural urban areas. LULC considerable impact on land-atmosphere/climate interactions. Over past two decades, numerous studies conducted in investigated areas field LULC. However, assemblage information missing some aspects. Therefore, provide coherent guidance, literature review scrutinize evaluate particular topical employed. This research study collected approximately four hundred articles five (5) interest, including (1) definitions; (2) classification systems used classify globally; (3) direct indirect changes meta-studies associated with LULC; (4) challenges knowledge gaps. The synthesis revealed definitions carried vital terms, at national, regional, global scales. Most were categories land changes. Additionally, analysis showed significant data consistency quality. gaps highlighted fall ecosystem services, forestry, data/image modeling Core findings exhibit common patterns, discrepancies, relationships from multiple studies. While as tool similarities among studies, our results recommend researchers endeavor perform further promote overall understanding, since investigations will continue

Language: Английский

---