A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia

Thoha AS, Triani H. 2021. A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia. Biodiversitas 22: 3319-3326. Fires often occur every dry season and have a significant impact on ecosystems and human activities. One of the important roles in reducing the risk of forest and land fires is the availability of updated vulnerability level maps in all vulnerable areas. The objective of this study was to determine the relationship between the driving factors for forest and land fires and hotspots and to obtain a spatial model of the distribution of vulnerability to forest and land fires in the Dairi District of North Sumatra Province. This study uses a composite mapping analysis method to obtain a spatial model and the distribution of vulnerable areas to forest and land fires. Six variables in the form of maps were used in building the model, including land cover, population density, distance from the road, distance from the river, and distance from the settlement. This study showed that the most important variable for vulnerability level model of forest and land fires was the distance from the settlement. This study also found that open land, the farthest distance to the road, the farthest distance to the river, the farthest distance to the settlement, and the densest population were the driving factors for increased fire activity. The spatial model of the vulnerability level to forest and land fires in Dairi District was Y = 0.022X1 + 0.214X2 + 0.113X3 + 0.482X4 + 0.169X5. Land cover having high-very high vulnerability level belonged to open land dominated by grass. The largest areas with a high-very high forest fire vulnerability level in Dairi District were spread over Tanah Pinem Sub-district.

Testing different remote sensing compositing periods for SOC content extraction in areas across Germany

<p>High spatial and temporal soil information is crucial to analyze soil developments and for monitoring long term changes to avoid soil degradation. A sufficient soil organic carbon (SOC) content is one of the key soil properties to achieve sustainable high productivity of soils, soil health and increased agroecosystem resiliency. For the usage of remote sensing approaches, naturally exposed soils in Germany occur rarely. Mainly agricultural regions can provide areas of exposed soils for short periods of time during a year. The Soil Composite Mapping Processor (SCMaP) is a fully automated approach to make use of per-pixel based bare-soil compositing to overcome the issue of limited soil exposure based on multispectral Landsat (TM 4, ETM 5, ETM+ 7 and OLI 8) imagery for individually determined time periods between 1984 and 2019.</p><p>Due to the high spatial and temporal resolution the SCMaP soil reflectance composites contain a considerable potential to derive detailed soil parameters as the SOC contents of exposed soils to add information to existing soil maps on field scale for areawide applications. Besides the soil reflectance composites several field soil samples provided by different federal authorities build the data base for the SOC modeling. Machine learning (ML) algorithms incl. Partial Least Squares and Random Forest regression with various inputs and set-ups are used and applied for several test areas in Germany. Furthermore, the capabilities of different compositing lengths (5-, 10- and 30-years) to derive spatial SOC contents are tested. The results and the validation of the different ML approaches and compositing lengths will be shown, providing insight into the benefits of this approach.</p>

PEMETAAN DAERAH RAWAN KEBAKARAN DI LAHAN GAMBUT BERDASARKAN POLA SEKUENS TITIK PANAS DI KABUPATEN PULANG PISAU KALIMANTAN TENGAH

Land and forest fire is one of the major that caused Indonesia’s deforestation, who has a significant impact to the environment, loss of conservation, air pollution and economic loss. This research makes a spatial modelling along with factor that can affect collerates the forest fire. Spatial model of vulnerability of land and forest fire is built by composite mapping analysis method. Hotspot that is used in this research is the results of data mining processing, with sequential pattern mining technique which to find the relationships between the occurances of sequential event and pattern that often appear. From the six variables that influence land and forest fire there are four variables that impacts on the study area, that is forest zone, depth of peatland, distance of irrigation, and distance of road. The fire in the area of study occurs many times in the peatland area with the depth of 400-800 cm. Land and forest fire occurs frequently in 100-900 meters from irrigation and land and forest fire also occurs frequently in 1-4 km form the road. Land and forest fire occurs frequently in protected forest

Pengurangan Resiko Kebakaran Hutan dan Lahan Melalui Pemetaan HGU dan Pengendalian Pertanahan (Studi Kasus Provinsi Riau)

Oil palm plantation expansion through inappropriate land clearing usually trigger forest fire and peat land fire in Riau Province. The purpose of this paper is to find the method to reduce disaster risk through preventive activities, conducted by mapping the distribution of Cultivation Right, and was overlaid with the map of disaster risk and agrarian control through location permit and control of spatial planning. The method used to produce disaster-prone area map was quantitative scoring and weighting, using Composite Mapping Analysis (CMA) method based on the relationship between factors with the percentage of fire spot (hotspot). The results show that from the distribution of cultivation right based on the level of vulnerability in Riau Province, there are 45 location of cultivation right lies along very high-risk area of forest fire with the total area of 95.260,7 hectares (10,4%); most of the area, counted for 70,4% with the area of 647.140,3 hectares covering 143 Cultivation Right location, located on the vulnerable area of forest fire; while 19,2% of the total cultivation right area are in less vulnerable area, spreading over 25 Cultivation Right location. Intisari: Ekspansi perkebunan sawit melalui land clearing yang tidak tepat seringkali memicu terjadinya kebakaran hutan dan lahan gambut di Provinsi Riau. Pengurangan resiko bencana melalui kegiatan preventif yaitu penyusunan peta sebaran HGU dioverlaykan dengan peta tingkat kerawanan bencana serta pengendalian pertanahan melalui ijin lokasi dan pengendalian melalui RTRW merupakan tujuan dari tulisan ini. Metode yang digunakan untuk menyusun peta kerawanan bencana adalah scoring dan pembobotan dilakukan secara kuantitatif menggunakan metode Composite Mapping Analysis (CMA) berdasarkan hubungan setiap faktor terhadap persentase titik api (hotspot). Hasil analisis menunjukkan bahwa dari sebaran HGU berdasarkan tingkat kerawanan di Provinsi Riau sebanyak 45 lokasi HGU berada pada daerah sangat rawan bencana kebakaran dengan total luasan 95.260,7 ha (10,4%); sebagian besar yaitu 70,4% dengan luasan 647.160,3 ha dengan sebaran sebanyak 143 HGU berada pada kawasan ancaman rawan terhadap bencana kebakaran hutan dan lahan; sementara 19,2% dari total luasan HGU berada pada kategori kurang rawan yang tersebar pada 25 HGU.