Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 300, P. 113918 - 113918

Published: Nov. 27, 2023

Language: Английский

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Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: March 1, 2023

The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from base year of 2001, NLCD releases database every 2-3-years. recent release NLCD2019 extends to 18 years. We implemented stratified random sample collect reference data for 2016 and 2019 components at Level II I classification hierarchy. For both dates, overall accuracies (OA) were 77.5% ± 1% (± value standard error) when agreement was defined as match between map label primary only, increased 87.1% 0.7% either or alternate label. At hierarchy, OA 83.1% 0.9% 90.3% also included in 5% higher compared component NLCD2016 only. No improvement realized by User's (UA) forest loss grass gain were>70% label, UA generally<50% all other change themes. Producer's (PA) water conducted post-analysis review map-reference identify patterns disagreement, these findings are discussed context potential adjustments mapping collection procedures that may lead improved accuracy going forward.

Language: Английский

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Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data

Remote Sensing of Environment, Journal Year: 2020, Volume and Issue: 247, P. 111935 - 111935

Published: June 23, 2020

With a growing number of Earth observation (EO) products available through operational programmes such as the European Union's Copernicus, there is increasing emphasis on product accuracy and uncertainty, necessitating evaluation against in situ reference measurements. Whilst existing datasets have proven valuable resource, they incorporate little data with which from recent EO instruments can be assessed. A reliance individual field campaigns has also led to several inconsistencies, whilst limiting extent temporal variations performance captured. Recently established environmental monitoring networks National Ecological Observatory Network (NEON), collect routine measurements using standardised protocols, provide promising opportunity this respect. The Copernicus Ground Based Observations for Validation (GBOV) service was initiated recognition fact. In first component project, raw observations been collected processed range land products. study, we focus leaf area index (LAI) fraction absorbed photosynthetically active radiation (FAPAR). Raw digital hemispherical photography (DHP) twenty NEON sites derive measurements, were then upscaled high spatial resolution maps. Using these data, assess recently released Global Land Service (CGLS) 300 m Version 1 (V1) derived PROBA-V, addition Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Infrared Radiometer Suite (VIIRS). When evaluated CGLS V1 demonstrated best agreement (RMSD = 0.57 LAI 0.08 FAPAR), followed by Collection 6 VNP15A2H MOD15A2H 0.81 0.89 0.12 FAPAR). Differing assumptions cause them sensitive slightly different quantities, are thought explain apparent biases over sparse vegetation forest environments. To ensure their continued utility, future work should updating GBOV implementing additional corrections, improving geographical representativeness.

Language: Английский

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Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Scientific Data, Journal Year: 2020, Volume and Issue: 7(1)

Published: Aug. 11, 2020

Abstract Although continental urban areas are relatively small, they major drivers of environmental change at local, regional and global scales. Moreover, especially vulnerable to these changes owing the concentration population their exposure a range hydro-meteorological hazards, emphasizing need for spatially detailed information on urbanized landscapes. These data be consistent in content scale provide holistic description layouts address different user needs. Here, we map United States into Local Climate Zone (LCZ) types 100 m spatial resolution using expert crowd-sourced information. There 10 LCZ types, each associated with set relevant variables such that represents valuable database properties. benchmarked against continental-wide existing novel geographic databases form. We anticipate dataset provided here will useful researchers practitioners assess how configuration, size, shape cities impact important human outcomes.

Language: Английский

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The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

Earth system science data, Journal Year: 2021, Volume and Issue: 13(11), P. 5127 - 5149

Published: Nov. 5, 2021

Abstract. Methane emissions from boreal and arctic wetlands, lakes, rivers are expected to increase in response warming associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane circumpolar scales has contributed a large uncertainty our understanding present-day future emissions. Here we present Boreal–Arctic Wetland Lake Dataset (BAWLD), dataset based on an expert assessment, extrapolated using random forest modelling available spatial climate, topography, soils, conditions, vegetation, surface water extents dynamics. In BAWLD, estimate fractional coverage five wetland, seven lake, three river classes within 0.5 × 0.5∘ grid cells that northern tundra biomes (17 % global surface). Land were defined criteria ensured distinct among classes, as indicated by co-developed comprehensive flux observations. wetlands occupied 3.2 106 km2 (14 domain) with 95 confidence interval between 2.8 3.8 km2. Bog, fen, bog most abundant wetland covering ∼ 28 each total area, while highest-methane-emitting marsh 5 12 %, respectively. Lakes, include all lentic open-water ecosystems regardless size, covered 1.4 (6 domain). Low-methane-emitting lakes (>10 km2) glacial jointly represented 78 lake high-emitting peatland yedoma 18 4 Small (<0.1 glacial, peatland, combined 17 area but disproportionally overall 0.15 0.38 Rivers streams estimated 0.12 (0.5 domain), which 8 was high-methane-emitting headwaters drain organic-rich landscapes. Distinct combinations spatially co-occurring identified across BAWLD domain, allowing mapping “wetscapes” have characteristic emission magnitudes sensitivities climate change at regional scales. With provide avoids double-accounting includes intervals class. As such, will be suitable many hydrological biogeochemical upscaling efforts region, particular those aimed improving assessments current Data freely https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Language: Английский

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Smokey the Beaver: beaver‐dammed riparian corridors stay green during wildfire throughout the western United States

Ecological Applications, Journal Year: 2020, Volume and Issue: 30(8)

Published: Sept. 3, 2020

Beaver dams are gaining popularity as a low-tech, low-cost strategy to build climate resiliency at the landscape scale. They slow and store water that can be accessed by riparian vegetation during dry periods, effectively protecting ecosystems from droughts. Whether or not this protection extends wildfire has been discussed anecdotally but examined in scientific context. We used remotely sensed Normalized Difference Vegetation Index (NDVI) data compare greenness areas with without beaver damming wildfire. include five large wildfires of varying burn severity dominant landcover settings western United States our analysis. found beaver-dammed corridors relatively unaffected when compared similar damming. On average, decrease NDVI fire is 3.05 times it beaver. However, plant rebounded year after regardless activity. Thus, we conclude that, while activity does necessarily play role post-fire resilience, significant resistance refugia creation.

Language: Английский

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Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive

Scientific Data, Journal Year: 2020, Volume and Issue: 7(1)

Published: Sept. 15, 2020

Field-level monitoring of crop types in the United States via Cropland Data Layer (CDL) has played an important role improving production forecasts and enabling large-scale study agricultural inputs outcomes. Although CDL offers type maps across conterminous US from 2008 onward, such are missing many Midwestern states or uneven quality before 2008. To fill these data gaps, we used now-public Landsat archive cloud computing services to map corn soybean at 30 m resolution Midwest 1999-2018. Our training were 2008-2018, validated predictions on 1999-2007 where available, county-level acreage statistics, state-level rotation statistics. The corn-soybean maps, which call Corn-Soy (CSDL), publicly hosted Google Earth Engine also available for download online.

Language: Английский

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Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China

Sensors, Journal Year: 2020, Volume and Issue: 20(10), P. 2757 - 2757

Published: May 12, 2020

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, sustainable development. Detecting predicting LUCC, a dynamic process, its driving factors will help in formulating effective land planning policy suitable for local conditions, thus supporting socioeconomic development environmental protection. In this study, taking Gansu Province as case study example, we explored LUCC pattern mechanism from 1980 to 2018, predicted 2030 using integrated LCM (Logistic-Cellular Automata-Markov chain) model data satellite remote sensing. The results suggest that was more reasonable second stage (2005 2018) compared with first (1980 2005). This because large area of green lands protected by ecological engineering stage. From general, natural were main force influencing changes Gansu, while effects not significant slow economy. Landscape indices analysis indicated under protection scenario would be favorable than historical trend scenario. Besides, present suggested arid semiarid could well detected model. hopefully provide theoretical instructions future management, new methodology reference regions.

Language: Английский

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Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2021, Volume and Issue: 175, P. 71 - 87

Published: March 14, 2021

The Sentinel-2 Level 2 Prototype Processor (SL2P) is made available to users for the retrieval of vegetation biophysical variables including leaf area index (LAI) from Multispectral Instrument (MSI) data within Sentinel Application Platform (SNAP). A limited number validation exercises have indicated SL2P LAI retrievals frequently meet user requirements over agricultural environments, but perform comparatively poorly heterogeneous canopies such as forests. Recently, a modified version was developed, using directional scattering factor (DASF) constrain an alternative regularisation (SL2P-D). Whilst makes use prior information on expected canopy conditions, SL2P-D trained uniform distributions input parameters define radiative transfer model (RTM) simulations. Using in situ measurements through Copernicus Ground Based Observations Validation (GBOV) service, we performed extensive and 19 sites throughout United States. For effective (LAIe), demonstrated good overall performance (RMSD = 0.50, NRMSD 31%, bias −0.10), with all meeting Sentinels Science (SEN4SCI) uncertainty homogeneous (cultivated crops, grasslands, pasture/hay shrub/scrub), whilst underestimation occurred (deciduous forest, evergreen mixed woody wetlands). reduced bias, slightly improving when compared 0.48, 30%, −0.05), indicating its approach appears offer some advantages information, especially at LAIe > 3. Additionally, resulted 32% more valid than SL2P, largest differences observed < 1. against opposed yielded similar patterns poorer 1.08 1.13, 49% 52%, −0.64 −0.68) because RTM used by does not account foliage clumping. In addition themselves, examined relationship between predicted uncertainties retrieved LAI. With respect LAIe, SL2P's were conservative, underestimating only 35% cases, those unbiased.

Language: Английский

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Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

ISPRS International Journal of Geo-Information, Journal Year: 2022, Volume and Issue: 11(3), P. 163 - 163

Published: Feb. 23, 2022

As the convenient outlet to Bo Sea and major region of economic development in Yellow River Basin, Shandong Province China has undergone large changes land use/land cover (LULC) past two decades with rapid urbanization population growth. The analysis LULC change patterns its driving factors section Basin can provide a scientific basis for rational planning ecological protection resources Basin. In this manuscript, we analyzed spatial pattern temporal 2000, 2010, 2020 by using random forest classification algorithm Google Earth Engine platform multi-temporal Landsat TM/OLI data. were also quantified factor detector interaction geodetector. Results show that decades, types study area are mainly farmland construction land, among which proportion decreased increased from 19.4% 29.7%. Based on results detector, it be concluded elevation, slope, soil type key affecting area. between elevation slope type, temperature precipitation strong explanatory power variation research data support environmental protection, sustainable, high-quality help local governments take corresponding measures achieve coordinated sustainable socioeconomic development.

Language: Английский

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