Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data

EVALUASI PRODUK MODIS GROSS PRIMARY PRODUCTION PADA HUTAN RAWA GAMBUT TROPIS INDONESIA (MODIS GROSS PRIMARY PRODUCTION EVALUATION IN TROPICAL PEAT SWAMP FOREST OF INDONESIA)

Metode Gross Primary Production (GPP) dikembangkan sebagai salah satu pendekatan perhitungan cadangan karbon yang tersimpan dalam vegetasi. Salah satu produk GPP yang secara operasional dapat diunduh secara cuma-cuma adalah MOD17 yang diperoleh dari Satelit Terra/Aqua MODIS, NASA. Mengingat produk ini masih bersifat global, maka upaya pengujian perlu dilakukan di beberapa tipe ekosistem. Baru-baru ini, NASA telah meluncurkan produk versi baru yang pengujiannya belum banyak dilakukan di hutan tropis, khususnya di wilayah Indonesia. Dalam penelitian ini dilakukan evaluasi MODIS GPP versi baru (MOD17A2-51) di hutan rawa gambut, Provinsi Kalimantan Tengah, menggunakan analisis time series dan uji statistik data lapangan (GPP_EC). Hasil penelitian menunjukkan bahwa data 8-harian MODIS GPP memiliki pola time series yang hampir sama dengan MOD-EC meskipun secara statistik memberikan korelasi yang kurang baik. Secara umum, MODIS GPP cenderung memiliki nilai yang lebih rendah dibandingkan GPP_EC baik pada musim hujan maupun pada musim kemarau. Sebaliknya, pada musim kemarau yang sangat panjang, seperti pada tahun 2002 akibat ENSO, nilai MODIS GPP cenderung overestimate dibandingkan GPP_EC. Walaupun demikian, nilai akumulasi GPP dengan mempertimbangkan musim (kemarau dan hujan) menunjukkan hubungan yang baik (r=0.94, RMS= 17.47, and Efficiency score= 0.68). Periode musim kering ke-2 (Agustus-Oktober) menunjukkan distribusi nilai yang lebih baik dibandingkan periode musim lainnya. Penelitian ini dapat menyimpulkan bahwa MODIS GPP versi 51 dapat digunakan untuk pemantauan kandungan biomasa berdasarkan musim di hutan rawa gambut tropis Indonesia. Kata Kunci: MODIS GPP, Karbon, Hutan rawa gambut tropis, Degradas

Fire Frequency and Related Land-Use and Land-Cover Changes in Indonesia’s Peatlands

Indonesia’s converted peatland areas have a well-established fire problem, but limited studies have examined the frequency with which they are burning. Here, we quantify fire frequency in Indonesia’s two largest peatland regions, Sumatra and Kalimantan, during 2001–2018. We report, annual areas burned, total peatland area affected by fires, amount of recurrent burning and associations with land-use and land-cover (LULC) change. We based these analyses on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua combined burned area and three Landsat-derived LULC maps (1990, 2007, and 2015) and explored relationships between burning and land-cover types. Cumulative areas burned amounted nearly half of the surface areas of Sumatra and Kalimantan but were concentrated in only ~25% of the land areas. Although peatlands cover only 13% of Sumatra and Kalimantan, annual percentage of area burning in these areas was almost five times greater than in non-peatlands (2.8% vs. 0.6%) from 2001 to 2018. Recurrent burning was more prominent in Kalimantan than Sumatra. Average fire-return intervals (FRI) in peatlands of both regions were short, 28 and 45 years for Kalimantan and Sumatra, respectively. On average, forest FRI were less than 50 years. In non-forest areas, Kalimantan had shorter average FRI than Sumatra (13 years vs. 40 years), with ferns/low shrub areas burning most frequently. Our findings highlight the significant influence of LULC change in altering fire regimes. If prevalent rates of burning in Indonesia’s peatlands are not greatly reduced, peat swamp forest will disappear from Sumatra and Kalimantan in the coming decades

Evaluasi produk Modis GPP di hutan tropis Indonesia

Salah satu tantangan dalam skema REDD (penurunan emisi dari deforestasidan degradasi hutan) adalah ditemukannya teknologi yang tepat dalam perhitunganstok karbon. Produk MODIS Gross Primary Production (GPP) menjadi salah satupendekatan alternatif untuk mengestimasi stok karbon di berbagai ekosistem. Akantetapi validasi terhadap produk ini perlu dilakukan agar diketahui tingkat akurasinyauntuk digunakan secara operasional. Penelitian ini bertujuan untuk memvalidasiproduk MODIS GPP, dan mengetahui sejauh mana produk ini dapat digunakan sebagaisumber acuan jumlah stok karbon di hutan tropis Indonesia. Data yang digunakanadalah MODIS GPP (MOD17), data meteorologi dan GPP dari lapangan tahun 2002-2005. GPP dari data lapangan diperoleh dari tower site di Palangkaraya, KalimantanTengah dengan menggunakan teknik Eddy Covariance (GPP_EC). Uji korelasi regresilinier dilakukan untuk mengetahui tingkat akurasi antara MODIS GPP dan GPP_EC.Hasil yang diperoleh menunjukkan bahwa tingkat akurasinya masih sangat rendahbahkan tidak memiliki korelasi khususnya pada tahun 2003-2005. Hal inidiperkirakan akibat besarnya error dari data meteorologi yang digunakan dalamalgoritma MODIS GPP, khususnya data radiasi. Di samping itu, tingginya kerapatanvegetasi dan nilai Fraction Photosynthetically Active Radiation (FPAR) yang terpengaruhnilai awan, juga diduga menjadi penyebab rendahnya korelasi ini. Meskipun total MODIS GPP cenderung underestimate terhadap GPP_EC, namun terdapat pola yanghampir sama pada jumlah tahunan GPP di antara keduanya.Hal.54-63 : ilus. ; 30 c

DROUGHT AND FINE FUEL MOISTURE CODE EVALUATION: AN EARLY WARNING SYSTEM FOR FOREST/LAND FIRE USING REMOTE SENSING APPROACH

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