Agrienvi : Jurnal Ilmu Pertanian
Vol 13 No 02 (2018): Vol 13 No 02 Desember Tahun 2018

PDF ESTIMASI DEGRADASI DAN DEFORESTASI HUTAN RAWA GAMBUT DENGAN CITRA PENGINDERAAN JAUH DI PROVINSI KALIMANTAN TENGAH Estimation of Degradation and Deforestation of Peat Swamp Forests with Remote Sensing Imagery in Central Kalimantan Province

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Article Info

Publish Date
23 Sep 2019

Abstract

ABSTRACTThe changed in 1995 ? 1998 when a programme of massive Peat Swamp Forest (PSF) conversion, the socalled Mega Rice Project (MRP) in Central Kalimantan was initiated to develop rice field has been impact bothdeforestation and forest degradation. The aim of this research is to classify forest degradation rates of PSFaccurately using multi-stage remote sensing methods through (1) Forest Canopy Density model (FCD) LANDSAT 5TM image acquired 2010 and supported by aerial photos interpretation, (2) inventoryineach forest cluster weredetermined by purposive sampling using 5 plotswith size of 20m x 20m.The results showed that there wasasignificant correlation(r)=0.82 between FCD and Canopy Area Index (CAI). Significant correlation was also shownbetween CAI and stand density, dominance andfrequency respectively with the value of r = 0.974; r = 0.937 and r =0.871. Based on these correlation, predicted that PSF on the researcharea has been changed become deforestation53.68%, very high degradation 3.37%, moderate high degradation 11.21%, middle degradation 15.30%, moderatelow degradation 15,45% and low degradation 0.99%.The conclusion thatthe PSF ecosystem of MRP largely hasbeen transformed intodegradation and deforetation areas.Keywords: Peat swamp forest, remote sensing, ecology, degradation and deforestation.

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Journal Info

Abbrev

aev

Publisher

Subject

Agriculture, Biological Sciences & Forestry

Description

Focus dan Scope AGRIENVI diterbitkan oleh Fakultas Pertanian Universitas Palangka Raya secara berkala setahun dua kali (bulan Juni dan Desember) yang memuat hasil penelitian, ulasan ilmiah, dan artikel di bidang Ilmu-ilmu Pertanian dan ...