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        <identifier>oai:figshare.com:article/33842647</identifier>
        <datestamp>2026-09-29T19:28:45Z</datestamp>
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          <dc:title>Code and data supporting: “Compositional turnover as an early warning signal of forest degradation and deforestation in Miombo-dominated landscapes”</dc:title>
          <dc:creator>MPANDA MUKENZA Médard (22098623)</dc:creator>
          <dc:subject>Environmental management</dc:subject>
          <dc:subject>Earth and space science informatics</dc:subject>
          <dc:subject>continuous land-cover mapping</dc:subject>
          <dc:subject>forest compositional dynamics</dc:subject>
          <dc:subject>Miombo woodland</dc:subject>
          <dc:subject>degradation definitions</dc:subject>
          <dc:subject>landscape fragmentation</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;Code and data supporting: "Compositional turnover as an early warning signal of forest degradation and deforestation in Miombo-dominated landscapes of Lualaba Province (DR Congo), 2016–2025" (submitted to GIScience &amp; Remote Sensing).&lt;/p&gt;&lt;p dir="ltr"&gt;The deposit includes:&lt;/p&gt;&lt;p dir="ltr"&gt;- Google Earth Engine, Python and R scripts for Sentinel-2 compositing, Random Forest classification and forest-mask generation, Multi-Output Neural Network (MONN) fractional-cover mapping, ecological-class and landscape-fragmentation analysis, degradation-definition sensitivity analysis, temporal-transferability and field validation, and the eight main figures.&lt;/p&gt;&lt;p dir="ltr"&gt;- The reference dataset of 2,333 Collect Earth Online plots used to train the MONN (spectral predictors, vegetation indices, texture metrics, fractional-cover labels and coordinates).&lt;/p&gt;&lt;p dir="ltr"&gt;- Tabular data underlying Table A1 and the field-validation data (60 plots) underlying Table C1.&lt;/p&gt;&lt;p dir="ltr"&gt;- GeoTIFF rasters: the binary forest mask defining the analysed forest area, and the MONN-predicted fractional composition of dry dense forest, gallery forest and Miombo woodland for 2016, 2019, 2022 and 2025.&lt;/p&gt;&lt;p dir="ltr"&gt;These materials are provided to support the transparency and reproducibility of the results presented in the manuscript.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-29T19:28:45Z</dc:date>
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