Background: Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models. Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass (AGB) of tropical dipterocarp forests (TDFs) in Kalimantan (Indonesian Borneo). However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision. This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets. Methods: We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations. We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form. Results: This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively. We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan. Through a validation using an independent dataset, we found that our equations were reliable in estimating tree AGB in TDF. The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models. Conclusions: Our equations improve the precision and reduce the bias of AGB estimates of TDFs. Local models developed from small samples tend to systematically bias. A validation of existing AGB models is essential before the use of the models.
& Key message This study assessed the effect of ecological variables on tree allometry and provides more accurate aboveground biomass (AGB) models through the involvement of large samples representing major islands, biogeographical
ABSTRAKHutan merupakan bagian dari ekosistem yang menyediakan jasa lingkungan bagi satu kesatuan ekosistem. Penurunan fungsi hutan dalam suatu ekosistem terjadi salah satunya karena deforestasi. Penelitian ini bertujuan membangun model spasial deforetasi di KPHP Poigar. Metode analisis deforestasi yaitu dengan analisis perubahan tutupan hutan menjadi tutupan bukan hutan dengan teknik post classification comparison. Analisis faktor penyebab deforestasi dilakukan dengan pembangunan model spasial deforestasi menggunakan model regresi logistik biner. Hasil penelitian menunjukkan bahwa luas penurunan tutupan hutan akibat deforestasi pada periode 2000 sampai 2013 yakni 12.668,2 hektare. Penyebab deforestasi di KPHP Poigar dipengaruhi oleh enam faktor yaitu jarak dari jalan, jarak dari pemukiman, jarak dari sungai, kepadatan penduduk, ketinggian tempat dan kemiringan lereng. Model regresi logistik dibangun menggunakan lima peubah penjelas yaitu jarak dari jalan, jarak dari sungai, kepadatan penduduk, ketinggian tempat dan kemiringan lereng. Kemampuan model dalam memprediksi deforestasi sebesar 58 % dari kejadian deforestasi aktual, sehingga model spasial deforestasi dapat menjadi salah satu sumber informasi untuk penyusunan arah pengelolaan KPHP Poigar kedepan.Kata kunci: Deforestasi, model regresi logistik, pemodelan spasial, KPHP Poigar ABSTRACT Forest is a part of the ecosystem that provides environmental services. Deforestation may decrease forest function in an ecosystem. This study aims to build a spatial model of deforestation in a forest management unit (FMU) of Poigar. Deforestation analysis carried out by analyze the change of forest cover into non-forest cover with post classification comparison technique. Driving forces of deforestation carried out by spatial modeling using binary logistic regression models (LRM). Result of logistic regression model was used to predict the deforestation in 2013 and
Forest and land fires are a high source of emissions in South Sumatera. In line with the national policy, South Sumatera Province commits in reducing emission, include emission from the forest and land fire. This research was aimed to assess carbon loss affected by fire in the year of 2015 that covered 3 districts in South Sumatera i.e Musi Banyuasin, Banyuasin, and Musi Rawas. The research was conducted by remeasurement of carbon stocks plots on 4 forests and land type i.e. secondary peat swamp forest, secondary dryland forest, bushes swamp, and forest plantation. Carbon stocks measuring are conducted on sample plots in a rectangular shape of 20 m x 50 m of size for various types of natural forest and a circle shape in the radius of 11.29 cm and 7.98 cm respectively for forest plantation of < 4 years and > 4 years old. Furthermore, carbon stocks in each plot are measured for 3 carbon pools of above-ground biomass, deadwood and litter. The result shows that carbon loss was varying on each forest and land type. The largest number of carbon loss occur on secondary peat swamp forest of 94.2 t/ha that equivalent to the emission of 345.4 t CO2eq. The second largest of carbon loss occur on secondary dryland forest of 36.3 t/ha following by forest plantation and bushes swamp of 18.5 t/ha and 13.5 t/ha. The largest carbon loss on secondary peat swamp forest and forest plantation occur on above-ground biomass pool but secondary dry forest and bushes swamp occur on the dead wood pool.
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