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Data · dataset · 2024

Land Use and Cover Maps, Central Kalimantan, Indonesia, 1990-2022

Listed in RIN Data Repository

Description

<p><strong>Data File Information&nbsp;</strong></p> <p>This dataset offers annual 30 m resolution land use and cover (LULC) maps for the western half of the Kapuas-Barito peatland hydrological unit (PHU) in Central Kalimantan, Indonesia, which also overlay parts of the historical Blocks A and E of the former Mega Rice Project. It comprises nine files (.tif and .shp formats), each representing a five-year interval between 1990 and 2022.

No map is available for 2014 due to heavy cloud cover; the 2015 and 2016 maps serve as substitutes. The 2016 map specifically captures major changes following the 2014&ndash;2015 fire events, aligning with our research project.</p> <p><strong>Data File Details</strong></p> <p>File names follow the pattern "lulc_m_ck_yyyy_v1", where:</p> <ul> <li>lulc denotes &ldquo;Land Use and Land Cover&rdquo;</li> <li>m represents the designated project site</li> <li>ck refers to Central Kalimantan</li> <li>yyyy is the dataset year</li> <li>v1 indicates version 1.</li> </ul> <p>Projection: EPSG:4326- Geographic</p> <p>Datum: WGS84</p> <p>Data type: Eight bit unsigned integer (for .tif)</p> <p><strong>LULC classes correspond to Gridcode values 1&ndash;7.</strong></p> <ol> <li>Pristine/degraded Peatswamp Forest (PSF)</li> <li>Secondary forest or tall shrubs</li> <li>Ferns, grass, or low shrubs</li> <li>Urban or bareland</li> <li>Water body</li> <li>Cloud/shadow/no-data</li> <li>Plantation (

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Note

Gridcode #7 for the Plantation class is available only from 2019)</li> </ol> <p><strong>Method</strong></p> <p>We adapted the classification approach of Vetrita and Cochrane (2021) with slight modifications, replacing Classification and Regression Trees (CART) with the Random Forest model (manuscript in preparation). The product was compared with SPOT-7 reference imagery from 17 October 2019. Overall, all classes achieved a producer&rsquo;s accuracy above 0.80, except for low vegetation (ferns, grass, or low shrubs) at 0.71.

As detecting forest changes (PSF to non-PSF) was the primary objective, the map attained the highest accuracy (0.99) for forests, as well as for water bodies and plantations. The lowest producer&rsquo;s accuracy (0.71) was recorded for low vegetation, while urban or bare land had the lowest user&rsquo;s accuracy (0.78).</p>

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Where it is published

Catalogue records · 1

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Provenance · 1 source records, 5 field assertions
SourceKeyLast seenRaw
RIN Data Repositoryhdl:20.500.12690/RIN/0SOBUS9 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:3709enrichment · rin brin go idtaxonomy-embedding@1.1.0title+keywords+description (69%)
concepts[field].local:field:earth-environmentalmapping · rin brin go idconnector:rin_brin_go_id@1.0.0
descriptionsource · rin brin go idconnector:rin_brin_go_id@1.0.0/metadata/dc/description
publication_datesource · rin brin go idconnector:rin_brin_go_id@1.0.0
titlesource · rin brin go idconnector:rin_brin_go_id@1.0.0/metadata/dc/title