Analysis and Applications of GlobeLand30: A Review

Chen, Jun; Cao, Xin; Peng, Shushi; Ren, Hongliang

doi:10.3390/ijgi6080230

Cited by 149 publications

(79 citation statements)

References 75 publications

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“…The comparison of the three different crop extent maps was performed to present the similarity among the three different cropland extent maps in three different agriculture field sizes of different continents (e.g., Australia, United States (US), South-Asia, and Africa) [19]. The similarity among the three crop extent maps were determined using the cropland landscape characteristics (e.g., look, area proportion, and clumping) mapped at different spatial resolutions and different size of agriculture fields (Figures 9-12).…”

Section: Comparison Of the Three Different Gfsad Cropland Extent Mapsmentioning

confidence: 99%

Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions

Yadav

Congalton

2018

Remote Sensing

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Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial resolutions, i.e., GFSAD1km, GFSAD250m, and GFSAD30m. An accuracy assessment and comparison of these three GFSAD cropland extent maps produced and published by different researchers was performed to establish their quality and reliability for monitoring croplands both at global and regional scales. Large area (i.e., global) assessment of GFSAD cropland extent maps was performed by dividing the entire world into regions using a stratification approach and collecting a reference dataset using a simple random sampling design. All three global cropland extent maps were assessed using a total reference dataset of 28,733 samples. The assessment results showed an overall accuracy of 72.3%, 80-98%, and 91.7% for GFSAD1km, 250 m (only for four continents), and 30 m maps, respectively. Additionally, a regional comparison of the three GFSAD cropland extent maps was analyzed for nine randomly selected study sites of different agriculture field sizes (i.e., small, medium, and large). The similarity among the three GFSAD cropland extent maps in these nine study sites was represented using a similarity matrix approach and two landscape metrics (i.e., Proportion of Landscape (PLAND) and Per Patch Unit (PPU)), which categorized the crop proportion and the crop pattern. A comparison of the results showed the similarities and differences in the cropland areas and their spatial extent when mapped at the three spatial resolutions and considering the different agriculture field sizes. Finally, specific recommendations were suggested for when to apply each of the three different GFSAD cropland extent maps for agriculture monitoring based on these agriculture field sizes.

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Section: Comparison Of the Three Different Gfsad Cropland Extent Mapsmentioning

confidence: 99%

Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions

Yadav

Congalton

2018

Remote Sensing

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“…Landcover data. GlobeLand30 landcover map, which was comprised of ten landcover types, including forests, artificial surfaces and wetlands, was used in this paper [38,39]. This map was extracted from more than 20,000 Landsat and Chinese HJ-1 satellite images.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Generation of High Resolution Vegetation Productivity from a Downscaling Method

Sun

Xiao

et al. 2018

Remote Sensing

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Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP. from single sensor limits their application in vegetation dynamics research and ecosystem services [2]. In this condition, advances and new methods are needed to exploit multi-scale data streams for producing time series high accuracy GPP and NPP in both a higher spatial and temporal resolution, which is also of great significance in studying vegetation productivity dynamics and carbon budgets.To produce high accuracy GPP and NPP datasets in both high spatial and temporal resolution, high quality input data with high resolution are needed. MODIS GPP and NPP products (MOD 17) were generated from a Light Use Efficiency (LUE) model from high quality MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products (R 2 = 0.6705 and RMSE = 1.1173 for the LAI product, R 2 = 0.8048 and RMSE = 0.1276 for the FPAR product against the Earth Observing System sites) [3,4]. The estimated Global LAnd Surface Satellite (GLASS) GPP and NPP products [5] were derived from high quality GLASS LAI and FPAR products (RMSE = 0.7848 and R 2 = 0.8095 for the LAI product, and RMSE = 0.1276 and R 2 = 0.8048 for the FPAR product against the Validation of Land European Remote sensing Instrument) [6,7]. But the contradiction between spatial and temporal resolution still exists in the inputs data for estimating high resolution GPP and NPP. Downscaling methods are good ways for blending the multi-scale remote sensing data and create predictions at a finer resolution than the inputs [8], therefore, have great potential in generating high resolution input data for vegetation productivity estimation. Traditional downscaling methods include image fusion methods, such as intensity-hue-saturation (IHS) [9], principal component substitution (PCS) [10], wavelet decomposition [11] and wave...

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“…GlobeLand30 (http://www.globallandcover.com/GLC30Download/index.aspx) is a global land cover map of 30m resolution produced by the National Geomatics Center of China and containing 10 land cover classes (full description of classes in (43)). The images used for GlobeLand30 classification are multispectral images, including the TM5 and ETM + of America Land Resources Satellite (Landsat) and the multispectral images of China Environmental Disaster Alleviation Satellite (HJ-1).…”

Section: Environmental Datamentioning

confidence: 99%

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Sedda

Lucas

Djogbénou

et al. 2018

Preprint

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Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we first propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations). These environmental data are used to delimit the area into ecologically homogenous strata. By employing a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous sampling campaigns. Notably, we found that a configuration of 30 locations with 4 households each (120 traps) will have a similar accuracy in the estimates of mosquito abundance as 300 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition / re-use in subsequent sampling campaigns. KeywordsMosquito sampling, stratification, lattice sampling design, model-based geostatistics, Sub-Saharan Africa. variance and Temp mean. Medium EVI mean, elevation and Precipitation. Red 20 Forest. Medium ET mean amplitude and variance, EVI mean, variance and amplitude, Temp mean and variance, Elevation and Precipitation; High Temp amplitude. Dark Green 25 Mixture of Cultivated land, Shrubland and Wetland. Low ET mean, EVI variance and Temp mean. Medium Elevation and Precipitation. Orange 35 Mixture of Shrubland and Grassland. Low Temp amplitude and Precipitation; Medium ET mean and variance, EVI mean, variance and amplitude, Temp mean and variance; High ET amplitude. Light green 45 Mixture of Shrubland, Grassland and Wetland. Low EVI amplitude, Temp variance and Precipitation; Medium ET mean and amplitude, EVI mean and variance, Temp mean and variance; High Temp mean and ET variance. Pink 55 Mixture of Urban, Forest, Wetland and Grassland. Low Temp mean; Medium ET mean, EVI mean and variance, and Precipitation; High Elevation. Yellow 60 Wetland. Low ET mean, EVI mean and variance; Medium Temp mean and Elevation; High Precipitation. Navy 65 Mixture of Urban, Tundra, Wetland, Water bodies and Grassland. Low ET variance and amplitude; Medium Elevation; High ET mean, EVI mean, variance and amplitude, Temp amplitude and Precipitation. Purple 75 Mixture of Water bodies and Urban. Low ET mean, EVI mean and amplitude, Temp variance and Precipitation; Medium ET amplitude and EVI variance; High ET variance and Temp mean. Sky 85 Mixture of Grassland, Water bodies and Urban. Low ET variance and amplitude, and Elevation; Medium Temp mean; High ET mean, EVI mean, variance and amplitude, Temp variance and Precipitation. Brown 95 Mixture of Wetland, Wate...

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Analysis and Applications of GlobeLand30: A Review

Cited by 149 publications

References 75 publications

Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions

Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions

Generation of High Resolution Vegetation Productivity from a Downscaling Method

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

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