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Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018

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doi.org2025-03-27 收录
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https://doi.org/10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284
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Land cover maps of Sierra de San Javier, Tucumán, Argentina, 1986-2018 This series of Land Cover Maps (LCM) was built using atmospherically corrected surface reflectance (TIER 1) Landsat 5 ETM and Landsat 8 OLI images, with less than 10% of cloud cover. A collection of Landsat 5 (1986, 1990, 2010) and Landsat 8 (2018) images from June to December, dividing images of each year in two periods were selected: dry (June to September) and wet seasons (September to December), to take advantage of seasonal phenological differences between native and exotic (L. lucidum) forests. Image collections were then combined by calculating the median of all values at each pixel across the stack of all matching bands per year, obtaining two images per year (dry/wet season). Land cover maps were constructed by applying the machine learning algorithm Random Forest (RF), based on an ensemble of classification trees, fully run on the Google Earth Engine platform. The maps include 8 land-use categories: Subtropical montane forest (SMF, category 40) Dry forest (DF, category 41) Montane grasslands (MGr, category 30) Anthropogenic grasslands and shrubland used for livestock and temporary agriculture, a mixed class including also herbaceous agriculture and low-density urban areas (AGr, category 31 ) Sugar cane (SC, category 51) Citrus plantations, mostly lemon (CP, category 50) High/medium-density urban areas (URB, category 11). Ligustrum lucidum (L, category 42). The extent of the maps are between -65,500 to -65.160 latitude to -26.600 to -26.970 longitude, GCS_WGS1984 and 30 x 30 m of resolution.

阿根廷图库曼省圣哈维尔山地的土地覆盖图集(1986-2018年),该图集通过大气校正表面反射率(TIER 1)的Landsat 5 ETM和Landsat 8 OLI影像构建而成,云量低于10%。该系列土地覆盖图(LCM)选取了1986年、1990年、2010年的Landsat 5影像以及2018年的Landsat 8影像(6月至12月),将每年影像分为两个时段:旱季(6月至9月)和雨季(9月至12月),以充分利用本地和外来(L. lucidum)森林之间的季节性物候差异。通过计算每年所有匹配波段中每个像素所有值的均值,将图像集合合并,每年得到两张图像(旱季/雨季)。土地覆盖图通过应用基于集成分类树的机器学习算法随机森林(RF),在Google Earth Engine平台上进行全面运行而构建。地图包括8种土地利用类别:亚热带山地森林(SMF,类别40)、干旱森林(DF,类别41)、山地草原(MGr,类别30)、用于牲畜和临时农业的人工草原和灌木丛,一个混合类别,包括草本农业和低密度城市区域(AGr,类别31)、甘蔗(SC,类别51)、柑橘种植园,主要种植柠檬(CP,类别50)、高/中密度城市区域(URB,类别11)和女贞(L,类别42)。地图的范围介于-65,500至-65.160纬度,-26.600至-26.970经度,使用GCS_WGS1984坐标系,分辨率为30 x 30米。
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