data_text

// Description for Datasets

exports.heat_hazard = "📖 Extreme Heat hazard\n\
is classified based on an existing\n\
and widely accepted heat stress\n\
indicator, the Wet Bulb Globe\n\
Temperature (WBGT, in °C), more\n\
specifically the daily maximum WGBT.\n\
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exports.kbdi = "📖 Keetch-Byram Drought\n\
Index (KBDI) is a continuous\n\
reference scale for estimating the\n\
dryness of the soil and duff layers.\n\
The index increases for each day\n\
without rain and vice versa.\n\
This index measure meteorological\n\
drought, with scale ranges from 0\n\
(no moisture deficit) to 800\n\
(extreme drought). This layer\n\
is limited to Asian Pacific counties.\n\
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exports.lst = "📖 This dataset contains\n\
atmospherically corrected surface\n\
reflectance and land surface\n\
temperature derived from the data\n\
produced by the Landsat 9 OLI/TIRS\n\
sensors. LST data is derived from B10\n\
directly among imageries collected\n\
during 2022 local summer daytime.\n\
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exports.vcf = "📖 The Terra MODIS Vegetation\n\
Continuous Fields (VCF) product is\n\
a sub-pixel-level representation of\n\
surface vegetation cover estimates\n\
globally. This layer shows values of\n\
the current year.\n\
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exports.evi = "📖 The MOD13Q1 V6.1 product Vegetation\n\
Index value at a per pixel basis.\n\
The Enhanced Vegetation Index (EVI)\n\
minimizes canopy background variations\n\
and maintains sensitivity over dense\n\
vegetation conditions. This layer\n\
shows median value for 2022 summer.\n\
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exports.hitisae = "📖 HiTiSAE is a gridded product\n\
that contains widely adopted\n\
human thermal-stress indices\n\
(ESI, HI, Humidex, WBGT, WBT,\n\
WCT, AT, NET), derived from\n\
ECMWF ERA5-Land and ERA5 reanalysis\n\
products, over South and East Asia\n\
in 2019 summer.\n\
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exports.worldcover = "📖 WorldCover provides the first\n\
global land cover products for\n\
2020 and 2021 at 10 m resolution,\n\
developed and validated in near-real\n\
time based on Sentinel-1 and Sentinel-2\n\
data.\n\
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exports.modis = "📖 In the MODIS dataset, The Land\n\
Surface Temperature (LST) and Emissivity\n\
daily data are retrieved at 1km pixels\n\
by the generalized split-window algorithm\n\
and at 6km grids by the day/night algorithm.\n\
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exports.uhi = "📖 This dataset contains summertime\n\
surface urban heat island (SUHI) intensities\n\
(unit degree Celsius) for day and night\n\
for over 10,000 urban clusters\n\
throughout the world. \n\
 【🖱️ Click heading to view project】";
 
exports.rwi = "📖 Provided by Facebook, the Relative \n\
Wealth Index (RWI) predicts the\n\
relative standard of living within\n\
countries using connectivity data\n\
and satellite imagery, via deep\n\
learning model. The RWI has\n\
proven useful for identifying pockets\n\
of poverty and potential beneficiaries\n\
 【🖱️ Click heading to view project】";

exports.cisi = "📖 Critical Infrastructure Spatial Index\n\
(CISI) represents the global spatial\n\
intensity of CI (transportation,\n\
energy, water, waste, telecommunication,\n\
education, and health). The dataset\n\
is generated from Open Streetmap (2021).\n\
It can be applied to explore the\n\
current landscape of CI, identify CI\n\
hotspots, and as exposure input\n\
for large-scale risk assessments.\n\
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exports.landscan = "📖 LandScan High-Definition (HD) is the\n\
finest resolution global population\n\
distribution data available and \n\
represents an ambient population.\n\
It is intended to aid in emergency\n\
preparedness, recovery missions, \n\
risk assessments and site\n\
suitability studies.\n\
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exports.urban = "📖 This urban density layer represents\n\
the spatial density of urban\n\
surface detected (% in 5km radius)\n\
from satellite imagery. It is\n\
extracted from ESA WorldCover\n\
the first global land cover\n\
products for 2020 and 2021\n\
at 10 m resolution.\n\
 【🖱️ Click heading to view project】";

exports.cropland = "📖 This cropland density layer\n\
represents the spatial density of\n\
agricultural fields detected\n\
(% in 5km radius) from satellite\n\
imagery. This layer is extracted\n\
from ESA WorldCover the first\n\
global land cover products for 2020\n\
and 2021 at 10 m resolution.\n\
 【🖱️ Click heading to view project】";

exports.forest = "📖 This forest density layer \n\
represents the spatial density of\n\
forest detected (% in 5km radius) \n\
from satellite imagery. This layer\n\
is extracted from ESA WorldCover\n\
the first global land cover products\n\
for 2020 and 2021 at 10 m resolution.\n\
 【🖱️ Click heading to view project】";

exports.ghsl = "📖 The GHS-SMOD is the rural-\n\
urban Settlement classification model\n\
adopted by the GHSL. The layer\n\
values range from 0 (inhabited)\n\
to 3 (high density clusters). It is the\n\
representation of the degree of\n\
urbanization concept into the\n\
GHSL data scenario.\n\
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exports.pdsi = "📖 This PDSI layer represents\n\
relative dryness from temperature\n\
and precipitation data. It is\n\
provided by TerraClimate, a dataset\n\
of monthly climate and climatic\n\
water balance for global terrestrial\n\
surfaces from 1958-2019. These data\n\
provide important inputs for ecological\n\
and hydrological studies at global\n\
scales that require high spatial\n\
resolution and time-varying data. \n\
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