Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td align="left">Class name </td><td align="center">WESTDC</td><td align="center">SCSLC</td><td align="center">GLC</td><td align="center">UMD</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td align="left">Evergreen needle leaved forest</td><td align="center">13174</td><td align="center">11532</td><td align="center">19250</td><td align="center">9075</td></tr><tr><td align="left">Deciduous needle leaved forest</td><td align="center">312</td><td align="center">4309</td><td align="center">25702</td><td align="center">10819</td></tr><tr><td align="left">Deciduous broad leaved forest</td><td align="center">36330</td><td align="center">32120</td><td align="center">33971</td><td align="center">67687</td></tr><tr><td align="left">Mixed forests</td><td align="center">6680</td><td align="center">3434</td><td align="center">123</td><td align="center">3529</td></tr><tr><td align="left">Closed shrublands</td><td align="center">7219</td><td align="center">4878</td><td align="center" rowspan="2">16212</td><td align="center">24049</td></tr><tr><td align="left">Open shrublands</td><td align="center">4198</td><td align="center">4945</td><td align="center">184674</td></tr><tr><td align="left">Grasslands</td><td align="center">267639</td><td align="center">234562</td><td align="center">325539</td><td align="center">265344</td></tr><tr><td align="left">Permanent wetlands</td><td align="center">18731</td><td align="center">18284</td><td align="center">10055</td><td align="center"> </td></tr><tr><td align="left">Croplands</td><td align="center">195069</td><td align="center">161682</td><td align="center">163214</td><td align="center">7038</td></tr><tr><td align="left">Urban and built up</td><td align="center">10274</td><td align="center">9059</td><td align="center"> </td><td align="center">5182</td></tr><tr><td align="left">Cropland/natural vegetation mosaic</td><td align="center">7419</td><td align="center">84276</td><td align="center"> </td><td align="center"> </td></tr><tr><td align="left">Barren or sparsely vegetated</td><td align="center">47482</td><td align="center">46324</td><td align="center">29255</td><td align="center">48860</td></tr><tr><td align="left">Water bodies</td><td align="center">11739</td><td align="center">10861</td><td align="center">2945</td><td align="center">9</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Comparison of different classification among the IGBPDIS, SCSLC, WESTDC, and UMD products (unit km<sup>2</sup>).

Advances in Meteorology

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Table 4: Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China 

Table 4 | Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China 