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Remote Sensing Applications: Society and Environment

ISSN: 2352-9385

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Datasets associated with articles published in Remote Sensing Applications: Society and Environment

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1970
2024
1970 2024
5 results
  • Data for: Can we locate shrimp aquaculture ponds from space? – A case study for Thailand
    This shapefile (generated with ArcGis Desktop Version 10.8) contains the identified water areas in Thailand that have a high likelihood to be a shrimp pond and the identified land cover change. -9999 = NoData Pond 1= Shrimp water area 2= Non-Shrimp water area Farm_Unit = Identification number of the corresponding "farm unit" Water_Cat =Water category obtained from the global water surface explorer (Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A. S., High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418.) 1=Permanent 2=New permanent 4=Seasonal 5=New seasonal 7=Seasonal to permanent 8=Permanent to seasonal 9=Ephemeral permanent 10=Ephemeral seasonal Coastal = defines the location 1 = inland region 2 = costal region Area_km2 = Area of the related polygon in km2; measured by using the WGS 1984 World Mercator projection. Forest_km2 = “primary forested land” area in km2, that has potentially been converted by shrimp pond construction NFores_km2 = “primary non-forested” area in km2, that has potentially been converted by shrimp pond construction NPrima_km2 = “non-primary land” area in km2, that has potentially been converted by shrimp pond construction likelihood = Attributed shrimp pond water area likelihood class Class 1 has the comparably lowest likelihood to be a shrimp pond and class 3 has the comparably highest likelihood to be a shrimp pond.
    • Dataset
  • Data for: Mapping Natural Forest Cover using Satellite Imagery of Nkandla Forest Reserve, KwaZulu-Natal, South Africa.
    The data is for the application of Support Vector Machine and Random Forest Algorithms to Landsat image for mapping the forest cover of a natural forest in South Africa.
    • Dataset
  • Data for:Monitoring Land Cover Changes in the Tropical High Forests using Multi-Temporal Remote Sensing and Spatial Analysis Techniques
    The uploaded research data include Digital Elevation Model of the study area, preprocessed Landsat imageries of the period under review (1990, 2013, and 2017) and their classified data which can be opened in any satellite processing software (ENVI, ERDAS, ArcGIS, etc.) The data also contain shapefiles which include the vector data of the study area, the training sites, and the reference control points used in determining the accuracy assessment. An excel spreadsheet has also been provided containing all the post classification analysis. Finally, all the maps that were generated have been added to the data in a jpeg format for visualization.
    • Dataset
  • Data for: Sensitivity of Vegetation to Annual Rainfall Variations over Nigeria
    This study investigated the spatial and temporal pattern in the sensitivity of vegetation to rainfall in Nigeria between 1980 and 2009. Rainfall data collected from 23 observation points were analysed and spatially correlated with monthly Normalized Difference Vegetation Index (NDVI) values for the same period to estimate the responsiveness of vegetation to annual variations in rainfall.
    • Dataset
  • Improved mapping of coastal salt marsh habitat change at Barnegat Bay (NJ, USA) using object-based image analysis of high-resolution aerial imagery_data
    Data for the classification of coastal land-cover of Barnegat Bay (Fig. 1) were downloaded from the USGS Earth Resources Observation and Science (EROS) Center via the USGS Earth Explorer website.
    • Dataset