Satellite-based burned area products are accurate for many regions. However, only limited assessments exist for Indonesia despite extensive burning and globally important carbon emissions. We evaluated the accuracy of four MODIS-derived (moderate resolution imaging spectroradiometer) burned area products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51), and their sensitivity to burned-area size and temporal window length used for detection. The products were compared to reference burned areas from SPOT 5 imagery using error matrices and linear regressions. The MCD45A1 product detected <1% of burned areas. The other products detected 38%–48% of burned area with accuracies increasing modestly (45%–57%) when smaller burns (<100 ha) were excluded, with MCD64A1 C6 performing best. Except for the MCD45 product, linear regressions showed generally good agreement in peatlands (R 2 ranging from 0.6 to 0.8) but detections were less accurate in non-peatlands (R 2 ranging from 0.2 to 0.5). Despite having higher spatial resolution, the FireCCI51 product (250 m) showed lower accuracy (OE = 0.55–0.88, CE = 0.33–0.50) than the 500 m MCD64A1 C6 product (OE = 0.43–0.79, CE = 0.36–0.51) but it was comparable to the C5.1 product (OE = 0.52–0.91, CE = 0.37–0.67). Dense clouds and smoke limited the accuracies of all burned area products, even when the temporal window for detection was lengthened. This study shows that emissions calculations based on burned area in peatlands remain highly uncertain. Given the globally significant amount of emissions from burning peatlands, specific attention is required to improve burned area mapping in these regions in order for global emissions models to accurately reflect when, where, and how much emissions are occurring.

No abstract

Indonesia is one of the countries in the world that is frequently affected by floods. Flood disasters can have various negative impacts and therefore need to be analyzed to determine prevention and mitigation measures. This study examined land cover change, flood detection, and flood distribution using multitemporal Sentinel-1 and Landsat-8 satellite imagery in the Barito watershed. A combination of change detection and the application of the Otsu algorithm was used to detect floodplains from Sentinel-1 imagery. Land use/land cover (LULC) changes are detected using a combination of change detection and machine learning in the form of a random forest algorithm. The overlay technique was used to analyze the distribution of floodplains. In this study, the floodplain in the study area was mapped to 109,623 ha. The change detection method detects a decrease in the areas of primary forest, secondary forest, fields, rice fields, shrubs and ponds, respectively, by 13,020 ha, 116,235 ha, 259 ha, 146,696 ha, 47,308 ha, and 9,601 ha. Settlements, bare land, plantations and water bodies increase by 14,879 ha, 64,830 ha, 218,916 ha, and 34,768 ha, respectively. Flooding was mainly found in the classes of rice fields, water bodies and primary forests.

The Remote Sensing Application Center – LAPAN has resulted in information on hotspot spreading. Availability of information monitoring the distribution of hotspots latest rapid, precise and accurate can improve the remote sensing application to support the management of forest resources and establish a monitoring system that is accountable and can be a reference in the organization of activities of forest fire control and land at the same time participate in managing, maintaining safety as well as the preservation of Indonesia’s natural resources is sustainable. Given the importance of information and communication in disaster crisis management, efforts to facilitate the implementation of dissemination of hotspots distribution information to the wider community should properly be realized. One application is to develop a Web-based dissemination system Geographic Information System with Geonode application. The method used in this research is a prototype with open technology. Geonode can integrate that information to the layers with web mapping systems and the internet. The opensource geoportal system is built, the result of a combination of Django framework and Python programming language that is capable of providing dynamic spatial visualization interactively and connected to other electronic information networks. This Dissemination System can be used for decision makers in preparation of planning, development, monitoring, and response to an emergency disaster of forest and land fires in Indonesia, as well as a reference in the field of innovative technology and application of Geospatial information.Keyword: hotspots, forest fires, emergency response, Geonode, dissemination, web applications  ABSTRAKPusat Pemanfaatan Pengideraan Jauh-LAPAN telah menghasilkan informasi sebaran titik panas. Ketersediaan informasi pemantauan sebaran titik panas terkini yang cepat, tepat dan akurat dapat meningkatkan pemanfaatan penginderaan jauh untuk mendukung pengelolaan sumber daya kehutanan dan membangun sistem pemantauan yang akuntabel dan dapat menjadi acuan dalam penyelenggaraan kegiatan pengendalian kebakaran hutan dan lahan sekaligus ikut serta dalam pengelolaan, menjaga keselamatan serta kelestarian sumber daya alam Indonesia yang berkelanjutan. Mengingat pentingnya informasi dan komunikasi dalam penanggulangan krisis akibat  bencana, maka upaya memudahkan pelaksanaan penyebarluasan informasi sebaran titik panas masyarakat luas dengan baik perlu diwujudkan. Salah satu pengaplikasiannya adalah dengan mengembangkan sistem diseminasi berbasis Web Sistem Informasi Geografis dengan aplikasi Geonode. Metode yang digunakan dalam penelitian ini adalah purwarupa dengan teknologi terbuka. Geonode dapat mengintegrasikan informasi tersebut dalam layer-layer sistem pemetaan web dan internet. Geonode merupakan sebuah sistem geoportal opensource gabungan antaran framework Django dan bahasa pemograman Python yang mampu menyajikan visualisasi spasial dinamis secara interaktif dan terhubung ke jaringan informasi elektronik lainnya. Sistem diseminasi ini dapat dipakai untuk pengambilan keputusan dalam persiapan perencanaan, pembangunan, pengawasan, dan respon terhadap keadaan darurat bencana kebakaran hutan dan lahan di Indonesia, serta sebagai referensi dibidang teknologi inovatif dan penerapan informasi Geopasial.Kata kunci: titik panas, kebakaran hutan, tanggap darurat, Geonode, diseminasi, aplikasi web

ABSTRAKInformasi luas area kebakaran sangat diperlukan sebagai salah satu pendekatan untuk penghitungan emisi gas rumah kaca. Data Landsat merupakan salah satu jenis citra penginderaan jauh optis resolusi menengah yang banyak dipergunakan untuk memetakan luas dan sebaran areal kebakaran. Tujuan penelitian adalah melakukan verifikasi hasil deteksi lahan bekas kebakaran hutan/lahan guna tersedianya hasil verifikasi burned area (BA) dari data Landsat-8 untuk dukungan penyusunan pedoman identifikasi BA. Pada penelitian ini dilakukan analisis verifikasi lahan bekas kebakaran yang diperoleh dari data satelit Landsat-8 sensor Operational Land Imager (OLI) menggunakan metode Normalized Burn Area (NBR). Data referensi yang digunakan dalam proses verifikasi adalah data lahan bekas kebakaran yang didelineasi dari citra SPOT-5. Citra ini memiliki resolusi spasial lebih tinggi dibandingkan dengan Landsat-8 OLI. Hasil penelitian menunjukkan bahwa tingkat akurasi Burned Area BA Landsat-8 OLI dengan metode ∆NBR memiliki nilai akurasi (overall accuracy) sebesar 87%, dengan commision error sebesar 2%, dan ommision error sebesar 11%. Tingkat akurasi burned area (BA) hasil estimasi dari data Landsat-8 dengan menggunakan metode ∆NBR memiliki nilai koefisien korelasi (r) 0,98 dengan persamaan Y = 0,928X -21,07 dan koefisien determinasi (R 2 ) = 0,96. Hasil ini menunjukkan bahwa sebesar 96% wilayah yang diklasifikasikan atau diestimasi sebagai wilayah yang terbakar adalah benar sebagai wilayah yang terbakar. Dengan demikian dapat disimpulkan bahwa metode ∆NBR yang diaplikasikan pada data Landsat-8 terbukti dapat digunakan untuk mendeteksi burned area. Kata Kunci: areal kebakaran, Landsat-8, Normalized Burn Area (NBR) ABSTRACT Information of burned area is needed as one among approaches on the calculation of greenhouse gas emissions. Landsat is one of the main types of remote sensing imageries frequently used to map the distribution of burned area.The purpose of this research is to verify the result of burned area (BA) analysis obtained from Landsat-8 satellite data acquired with

Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies.

This study evaluated two parameters of fire danger rating system (FDRS) using remote sensing data i.e. drought code (DC) and fine fuel moisture code (FFMC) as an early warning program for forest/land fire in Indonesia. Using the reference DC and FFMC from observation data, we calculated the accuracy, bias, and error. The results showed that FFMC from satellite data had a fairly good correlation with FFMC observations (r=0.68, bias=7.6, and RMSE=15.7), while DC from satellite data had a better correlation with FFMC observations (r=0.88, bias=49.91, and RMSE=80.22). Both FFMC and DC from satellite and observation were comparable. Nevertheless, FFMC and DC satellite data showed an overestimation values than that observation data, particularly during dry season. This study also indicated that DC and FFMC could describe fire occurrence within a period of 3 months before fire occur, particularly for DC. These results demonstrated that remote sensing data can be used for monitoring and early warning fire in Indonesia.

No abstract