- Thünen-Institut für Internationale Waldwirtschaft und Forstökonomie
(Thünen Institute of International Forestry and Forest Economics)
Leuschnerstraße 91
21031 Hamburg (Germany)
- Remote Sensing, Remote sensing and GIS applications in Landscape Research, Remote sensing and GIS, Geomatics, Remote Sensing (Earth Sciences), Environmental Remote Sensing, and 20 moreGroundwater Resources Modelling Using Gis and Remote Sensing, Hyperspectral remote sensing, Satellite Remote Sensing (Earth Sciences), Forest Ecology, Comparison of soft classification techniques for forest cover mapping, Palsar Classification, Fusion Remote Sensing Palsar, Remote Sensing and GIS applications in Forestry, Forest biometrics, global Climate change, Community-Based Participatory Research, Climate Change and Its Impact on Food Security and Agriculture, LiDAR for Forestry, Radar, Synthetic Aperture Radar, Ecosystems Services, Climate Change, Geography, Environmental Sustainability, and Climate Change in Developing Countriesedit
- Arief Wijaya received his doctoral degree in the field of Remote Sensing from TU-Bergakademie Freiberg, Germany in 20... moreArief Wijaya received his doctoral degree in the field of Remote Sensing from TU-Bergakademie Freiberg, Germany in 2010. He was a recipient of Georg Forster Research Fellowship from Alexander von Humboldt Foundation in 2011 undertaking Post-Doctoral research at TU-Dresden, Germany to model carbon biomass over tropical peat swamp forest of Indonesia.
Prior to his tenure at Thünen Institute, he worked at Center for International Forestry Research (CIFOR) based in Indonesia, and is responsible to integrate socio-economic and spatial data for analyzing drivers of deforestation and forest degradation in Indonesia, Vietnam, Peru, Brazil, Cameroon and Zambia. He was also an observer of UN Climate negotiations, especially for MRV REDD+ and climate change mitigation issues. In 2015, he is part of technical team of Ministry of Environment and Forestry in Indonesia responsible for preparing National Forest Reference Emissions Level (FREL) document for submission to UNFCCC.
He is currently working within Landscape Forestry in the Tropics (LaForeT) project established at Thünen Institute of International Forestry and Forest Economics in Hamburg, Germany and focuses on the analysis of drivers of deforestation/reforestation and spatial modeling of tropical landscape dynamics in the Philippines and Ecuador.edit
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Research Interests: Geography, Environmental Science, Forestry, Remote Sensing, Principal Component Analysis, and 15 moreBiomass, Linear Model, Land Cover, Tropical forest, Transect, Forest biomass, Vegetation indices, THERMAL INFRARED REMOTE SENSING DATA, Statistical Test, Texture Features, Above ground biomass, Linear Equations, Maximum Likelihood Method, remote sensing data, and Field Measurement
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<p>Histogram distribution of propensity scores before and after matching between timber concessions and protected areas (left panel); and between timber and oil palm concessions (right panel).</p
Research Interests: Business, Geography, Cartography, Forestry, Agroecology, and 15 moreEcology, Medicine, Conservation Science, Earth and Environmental Sciences, Panel Data, Biological Sciences, Environmental Impacts, Matching, Environmental protection, Agricultural Production, Palm Oil, Areas, GIS, Concessions, and Matching statistics
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"This research presents an evaluation, to which degree geological and geomorphological features can be characterized from multisource of remote sensing data such as Hyperion and Advanced Landing Imager (ALI) onboard EO-1, High... more
"This research presents an evaluation, to which degree geological and geomorphological features can be characterized from multisource of remote sensing data such as Hyperion and Advanced Landing Imager (ALI) onboard EO-1, High Resolution visible onboard the Systeme Pour l'Observation de la Terre (SPOT) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard TERRA. The Chitral area is steep and a highly rugged terrain with sparse vegetation and low relief and is an isolated and remote region in the Eastern Hindukush (Northern Pakistan and Eastern Afghanistan border). This Region lies at an altitude of 1500m upwards in the North West Frontier Province of Pakistan, on the border with Afghanistan. The four great mountain ranges in Asia i.e. Hindukush, Pamir, Karakorum, and the Himalayas are merged here. The highest peak of the Hindukush is Tirich Mir 7,787m. In this investigation, we intend to analyze the potential of multitude source of remote sensing data to characterize geological, geomorphological features and mineral identification. Although some previous image-processing techniques have been used to map this area, a better understanding of the potential of different kind of remote sensing data in such environments is still inevitable. In order to characterize geological features, we apply the Minimum Noise Fraction-Transformation (MNF) for data quality assessment and noise reduction as well as Spectral Angle Mapper (SAM) for classification purposes. Input data for this step were atmospherically and geometrically corrected from the already mentioned sensors. The selection of the classes for the classification purpose was based on previously published geological maps and the field visit of the of the study area supported by high spatial resolution from ALI and SPOT data. Preliminary results showed that Hyperion data were useful for lithological and the geological structure examination combined with high resolution data, enabling us to characterize lithologic units. However, classification results were affected by the steeply topography observed mainly with the use of a DEM generated from ASTER data. Rugged topography of this region was responsible for reflectance value changes due to different degrees of exposure of the units and the generation of spectral endmembers with some degrees of uncertainties. Such uncertainties were also strongly dependent on the data acquisition and spatial resolution. Further investigations will include illumination and exposure effect correction based on a non-Lambertian assumption as well as seasonal remote sensing data analysis."
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Provision of accurate forest parameter properties is important as a basis for forest resources monitoring and carbon cycle assessment. The present study aims to model leaf area index (LAI), above ground biomass and carbon stocks over... more
Provision of accurate forest parameter properties is important as a basis for forest resources monitoring and carbon cycle assessment. The present study aims to model leaf area index (LAI), above ground biomass and carbon stocks over tropical peatland forests using single polarization SAR, full polarimetry SAR (PolSAR) data. Single band ALOS Palsar data (HH band, acquired on November 17, 2008) and polarimetric data (HH, VV, HV and VH, collected on April 4 and May 5, 2007) are used for the study. A series of ENVISAT ASAR data (5 datasets) collected in 2004 – 2005 are also used to model the forest properties. Landsat ETM data collected on January 22, 2009 is also used as a reference. The relationship between forest parameters and normalized radar backscattering is estimated using empirical models, and preliminary results show that Polarimetric SAR data has better correlations with the LAI and biomass than single polarimetry SAR data. The field data were collected during field work in ...
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This work aims to estimate Above Ground biomass (AGB) of a tropical rainforest in East Kalimantan, Indonesia using equation derived from the stand volume prediction and to study the spatial distribution of AGB over aforest area. The... more
This work aims to estimate Above Ground biomass (AGB) of a tropical rainforest in East Kalimantan, Indonesia using equation derived from the stand volume prediction and to study the spatial distribution of AGB over aforest area. The potential of remote sensing and field measurement data to predict stand volume and AGB were studied Landsat ElM data were atmospherically corrected using Dark Object Subtraction (DOS) technique, and topographic corrections were conducted using C-correction method Stand volume was estimated using field data and remote sensing data using Levenberg-Marquardt neural networks. Stand volume data was converted into the above ground biomass using available volume - AGB equations. Spatial distribution of the AGB and the error estimate were then interpolated using kriging. Validated with observation data, the stand volume estimate showed integration of field measurement and remote sensing data has better prediction than the solitary uses of those data. The AGB est...
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Research Interests: Environmental Science, Environmental Economics, Climate Change, Carbon, Citizen Journalism, and 15 moreAgriculture, Food Security, Economy, Incentive, Greenhouse Gas, Mesure, Changement Climatique, Gestion Des Ressources Naturelles, Forêt, Carbon Fibers, Emission, Carbone, Equity Law, Déboisement, and Adaptation Aux Changements Climatiques
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We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan‐tropical AGB map... more
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan‐tropical AGB map at 1‐km resolution using an independent reference dataset of field observations and locally calibrated high‐resolution biomass maps, harmonized and upscaled to 14 477 1‐km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB ...
Research Interests: Environmental Science, Remote Sensing, Climate Change, Biomass, Remote sensing and GIS applications in Landscape Research, and 12 moreGlobal Change Biology, Medicine, Forest inventories, Biological Sciences, Environmental Sciences, Trees, Carbon balance, Satellite Imagery, Theoretical Models, Tropical forests, Tropical Climate, and Remote Sensing and GIS applications in Forestry
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Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen... more
Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende...
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The Conference of the Parties (COP) under the United Nations Framework Convention on Climate Change (UNFCCC) invites developing countries aiming to undertake REDD+ activities to provide a number of strategic documents. Indonesia accepts... more
The Conference of the Parties (COP) under the United Nations Framework Convention on Climate Change (UNFCCC) invites developing countries aiming to undertake REDD+ activities to provide a number of strategic documents. Indonesia accepts the invitation to submit proposed national forest reference emission level (FREL) for deforestation and forest degradation in the context of results-based payments for activities relating to REDD+. The FREL in this publication attempts to improve the previous FRELs, which was developed under three initiatives (Second National Communication/SNC, REDD+ Agency/RA, and Ministry of Forestry/MoFor) and fulfill the COP requirements by following the guidance for technical assessment and adopting principals on transparency, accuracy, completeness and consistency. Experts representing cross-ministerial agencies and organizations were commissioned to facilitate the construction process through a transparent and scientific-based participatory mechanism. Stepwise...
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The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing.... more
The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing. The participation of local people has been explored in carbon emission Measurement, Reporting and Verification (MRV) for REDD+. The feasibility and sustainability of participatory MRV (PMRV) are not automatic and depend on its relevance to local people (including incentives to participate), their technical capacity and the existence of appropriate structures for MRV.
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Abstract Reference levels (RLs) are key points that determine the starting point of carbon credits payment within REDD+ framework. This work approaches the RLs using remote sensing data and analysis. Objectives of this study are: 1)... more
Abstract Reference levels (RLs) are key points that determine the starting point of carbon credits payment within REDD+ framework. This work approaches the RLs using remote sensing data and analysis. Objectives of this study are: 1) mapping of forest cover change and 2) predicting carbon emissions/removals as a basis of RLs assessment. Forest cover change was estimated using national land cover map from the Ministry of Forestry in Indonesia. Combined with social economic parameters, deforestation drivers were ...
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Research Interests: Environmental Science, Computer Science, Forestry, Remote Sensing, Machine Learning, and 15 moreNeural Network, Kalman Filter, Geoscience and remote sensing, Image Classification, Land Cover, Tropical forest, Support vector machine, Land cover classification, Above ground biomass, Physical Properties, Scaled Conjugate Gradient, Classification Accuracy, Maximum Likelihood Method, Allometric equation, and Dry Season
Research Interests: Geography, Environmental Science, Image Classification, Key words, Spatial Information, and 14 moreRainforest, Support vector machine, Texture Classification, Land cover classification, Spatial Variability, Tropical Rainforests, Gray Level Co-occurrence Matrix, Tropical rainforest, Tropical rain forest, Accuracy Assessment, Minimum Distance Inference, Fractal Dimension, Semivariogram, and remote sensing data
Research Interests: Geography, Cartography, Forestry, Decision Making, Agroecology, and 15 moreAgroforestry, Indonesia, Ecology, Conservation Science, Earth and Environmental Sciences, Biological Sciences, Environmental Impacts, Indonesian, Environmental protection, Agricultural Production, Borneo, Illegal Logging, Conservation of Natural Resources, Forest Cover, and GIS
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Deforestation, which is mainly caused by illegal logging, is a serious problem in Indonesia. Illegal logging is closely related to the quality of management, therefore it is one of the main factors that can hinder the sustainability of... more
Deforestation, which is mainly caused by illegal logging, is a serious problem in Indonesia. Illegal logging is closely related to the quality of management, therefore it is one of the main factors that can hinder the sustainability of forest management. This study aimed at the development of method that provides more reliable detection of illegal logging in a form of single tree felling by means of multi-stage classification of multi-source data, in Labanan Forest Management Unit in East Kalimantan, Indonesia.
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Forest degradation (FD) is an important component of carbon emissions in many developing countries. According to Cancun agreement, countries are required to develop MRV system that allows to account for FD related loss or gain of carbon... more
Forest degradation (FD) is an important component of carbon emissions in many developing countries. According to Cancun agreement, countries are required to develop MRV system that allows to account for FD related loss or gain of carbon stocks. This study assessed the ability of quad-polarimetric L-band Synthetic Aperture Radar (SAR) data and polarimetric SAR features aiming at identifying forest degradation events on tropical peat swamp forests in SE Asia region. The selected study site is on peatland forests in Kampar Peninsula, Riau Province, Sumatera, characterized with different forest disturbance, from forest plantation and oil palm concessions. Radar backscatter data (i.e. HH, HV, VH and VV), SAR polarimetric decomposition features (i.e. alpha angle, entropy and anisotropy), ratio of volume – ground scattering amplitude and combined scattering matrix element values were used as ancillary data of the classification. Applying maximum likelihood classification (MLC) method, the ...
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This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis... more
This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensi...
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This study applied a geostatistical approach to quantify above-ground biomass (AGB) of the Labanan Concession Forest in East Kalimantan, Indonesia. Forest inventory data collected via transect sampling were converted to AGB, and two... more
This study applied a geostatistical approach to quantify above-ground biomass (AGB) of the Labanan Concession Forest in East Kalimantan, Indonesia. Forest inventory data collected via transect sampling were converted to AGB, and two approaches of estimating the spatial distributions of biomass, the global and stratified approaches, were compared. The global approach does not take local varying structures into account, whereas the stratified approach accounts for the heterogeneity of land cover types. Thus, AGBs estimated from each land cover type were pooled for the stratified approach. Ordinary kriging was performed to predict AGB at unsampled locations. The total estimates of AGB and RMSCVEs for the global and stratified methods were 13,512,392.2 tons (161.92 ton/ha) and 13,607,205.5 tons (163.05 ton/ha), respectively, for AGB and 81.0 ton/ha and 81.2 ton/ha, respectively, for RMSCVE. Considering the different environmental conditions for each land cover type, the stratified metho...
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EXECUTIVE SUMMARY ▪ The world is not on track to limit the catastrophic impact of climate change. The United Nations Framework Convention on Climate Change (UNFCCC) calls on signatory countries to develop more ambitious long-term plans... more
EXECUTIVE SUMMARY ▪ The world is not on track to limit the catastrophic impact of climate change. The United Nations Framework Convention on Climate Change (UNFCCC) calls on signatory countries to develop more ambitious long-term plans for climate actions by 2020. ▪ Indonesia’s self-interest would be served by a longterm strategy (LTS) for climate action that looks beyond the next 5–10 years; such a strategy could secure needed growth while considering conditions and risks beyond 2030. ▪ This working paper offers a preliminary overview of the benefits and urgency of producing an LTS for climate action in Indonesia and highlights current opportunities to develop an effective strategy. The assessment is derived from a review of the literature and interviews with experts and government officials. ▪ Some current initiatives can serve as a basis for Indonesia’s LTS, such as the low-carbon development initiative of the Ministry of National Development Planning (Badan Perencanaan Pembangun...
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This study predicts global forest cover change for the 1980s and 1990s from AVHRR time series metrics in order to show how the series of consistent land cover maps for climate modeling produced by the ESA climate change initiative land... more
This study predicts global forest cover change for the 1980s and 1990s from AVHRR time series metrics in order to show how the series of consistent land cover maps for climate modeling produced by the ESA climate change initiative land cover project can be extended back in time. A Random Forest model was trained on global Landsat derived samples. While the deforestation was underestimated by the model, major global patterns were effectively reproduced. Compared to reference data for the Amazon satisfying accuracies (>0.8) were achieved, but results are less promising for Indonesia.
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This paper considers the concept of a “data loop” —a relationship between the government and the private sector that focuses on strengthening collaborative efforts to enhance climate ambition through data sharing—in the Indonesian... more
This paper considers the concept of a “data loop” —a relationship between the government and the private sector that focuses on strengthening collaborative efforts to enhance climate ambition through data sharing—in the Indonesian context. In the data loop, the private sector shares climate and GHG emissions data with the government, and the government facilitates mandatory reporting systems to properly collect this data and incentivize voluntary reporting, further encouraging more robust data sharing. A data loop could generate the impetus for the private sector to provide the data required, and the government could, in turn, provide greater clarity through more robust assessments of efforts and projections, which would be supported by strengthened domestic and international measurement, tracking, reporting, regulation, and verification systems. The concept of the data loop builds upon previous research suggesting that a similar relationship could lead to enhanced climate action: a...
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应用遥感技术, 地理信息系统和野外观测数据, 评估了热带森林环境下地上生物量和木材蓄积量. 用于模拟森林属性的这些数据具有地理特异性和高度的不确定性, 因此, 这方面需要开展更多的研究工作. 选取了16 个试样地带1460 个样地, 测定树木胸径及其他用于评估生物量的其他森林属性. 本实验在印尼加里曼丹东部的热带雨林开展. 应用现有的胸径-生物量公式来评估地上生物量密度. 估测值在研究区修正的GIS 地图上重叠显示. 计算各种地被物的生物量密度.... more
应用遥感技术, 地理信息系统和野外观测数据, 评估了热带森林环境下地上生物量和木材蓄积量. 用于模拟森林属性的这些数据具有地理特异性和高度的不确定性, 因此, 这方面需要开展更多的研究工作. 选取了16 个试样地带1460 个样地, 测定树木胸径及其他用于评估生物量的其他森林属性. 本实验在印尼加里曼丹东部的热带雨林开展. 应用现有的胸径-生物量公式来评估地上生物量密度. 估测值在研究区修正的GIS 地图上重叠显示. 计算各种地被物的生物量密度. 用样品数据子集表达遥感方法来形成地上生物量和材积线性方程模型. 皮尔森相关统计检验 ...
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The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing.... more
The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing. The participation of local people has been explored in carbon emission Measurement, Reporting and Verification (MRV) for REDD+. The feasibility and sustainability of participatory MRV (PMRV) are not automatic and depend on its relevance to local people (including incentives to participate), their technical capacity and the existence of appropriate structures for MRV.
The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing.... more
The participation of local people in climate change mitigation is considered key to ensuring that their priorities are taken into account. This should help to design effective social safeguards and to improve equity in benefit sharing. The participation of local people has been explored in carbon emission Measurement, Reporting and Verification (MRV) for REDD+. The feasibility and sustainability of participatory MRV (PMRV) are not automatic and depend on its relevance to local people (including incentives to participate), their technical capacity and the existence of appropriate structures for MRV.
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We combined two existing datasets of vegetation aboveground biomass (AGB) (Saatchi et al., 2011; Baccini et al., 2012) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and... more
We combined two existing datasets of vegetation aboveground biomass (AGB) (Saatchi et al., 2011; Baccini et al., 2012) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally-calibrated high-resolution biomass maps, harmonized and upscaled to 14,477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N – 23.4 S) of 375 Pg dry mass, 9% - 18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2,118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15 – 21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.
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Monitoring of forest cover and forest functions provides information necessary to support policies and decisions to conserve, protect and sustainably manage forests. Especially in the tropics where forests are declining at a rapid rate,... more
Monitoring of forest cover and forest functions provides information necessary to support policies and decisions to conserve, protect and sustainably manage forests. Especially in the tropics where forests are declining at a rapid rate, national forest monitoring systems capable of reliably estimating forest cover, forest cover change and carbon stock change are of vital importance. As a large number of tropical countries had limited capacity in the past to implement such a system, capacity building efforts are now ongoing to strengthen the technical and political skillsets necessary to implement national forest monitoring at institutional levels. This paper assesses the current status and recent changes in national forest monitoring and reporting capacities in 99 tropical countries, using the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2015 data, complemented with FRA 2010 and FRA 2005 data. Three indicators “Forest area change monitoring and remote sensing capacities”, “Forest inventory capacities” and “Carbon pool reporting capacities” were used to assess the countries’ capacities for the years 2005, 2010 and 2015 and the change in capacities between 2005–2010 and 2010–2015. Forest area change monitoring and remote sensing capacities improved considerably between 2005 and 2015. The total tropical forest area that is monitored with good to very good forest area change monitoring and remote sensing capacities increased from 69% in 2005 to 83% in 2015. This corresponds to 1435 million ha in 2005 and 1699 million ha in 2015. This effect is related to more free and open remote sensing data and availability of techniques to improve forest area change monitoring. The total tropical forest area that is monitored with good to very good forest inventory capacities increased from 38% in 2005 to 66% in 2015. This corresponds to 785 million ha in 2005 and 1350 million ha in 2015. Carbon pool reporting capacities did not show as much improvement and the majority of countries still report at Tier 1 level. This indicates the need for greater emphasis on producing accurate emission factors at Tier 2 or Tier 3 level and improved greenhouse gases reporting. It is further shown that there was a positive adjustment in the net change in forest area where countries with lower capacities in the past had the tendency to overestimate the area of forest loss. The results emphasized the effectiveness of capacity building programmes (such as those by FAO and REDD+ readiness) but also the need for continued capacity development efforts. It is important for countries to maintain their forest monitoring system and update their inventories on a regular basis. This will further improve accuracy and reliability of data and information on forest resources and will provide countries with the necessary input to refine policies and decisions and to further improve forest management.
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Traditional spectral classification of remote sensing data applied on per pixel basis ignores the potentially useful spatial information between the values of proximate pixels. Although spatial information extraction has been greatly... more
Traditional spectral classification of remote sensing data applied on per pixel basis ignores the potentially useful spatial information between the values of proximate pixels. Although spatial information extraction has been greatly explored, there have been limited attempts to enhance classification by combining spectral and spatial information. This improvement would arise from the hypothesis that a pixel is not independent of its neighbors and, furthermore, that its dependence can be quantified and incorporated into the classifier. This study aims to explore the potential of utilizing texture spatial variability using Geostatistics
and Grey Level Co-occurrence Matrix (GLCM) texture measures. Different texture layers derived from Geostatistics method, namely fractal dimension, semivariogram, madogram, rodogram, pseudo-cross variogram and pseudo-cross madogram, were incorporated for the land cover classification of tropical rainforests in East Kalimantan, Indonesia. Texture layers of grey level 2 co-occurrence matrix (GLCM) channels, i.e. variance, contrast, dissimilarity, and homogeneity, were also used for the classification. Two classification methods, using Support Vector Machine and Minimum distance were applied for image classification. Landsat 7 ETM images combined with textural information is used for land cover classification of tropical rainforest area. Band 5 of Landsat data was used to compute texture layers using the GLCM and Geostatistics methods. This band was chosen because it has the highest variance of training data compared to other spectral bands. The results were compared to find out how the extra information given by the texture enhances the classification. According to the accuracy assessment using error matrix, combinations of image and texture data performed better with 81% of accuracy compared to those of image data only with 76% of accuracy.
and Grey Level Co-occurrence Matrix (GLCM) texture measures. Different texture layers derived from Geostatistics method, namely fractal dimension, semivariogram, madogram, rodogram, pseudo-cross variogram and pseudo-cross madogram, were incorporated for the land cover classification of tropical rainforests in East Kalimantan, Indonesia. Texture layers of grey level 2 co-occurrence matrix (GLCM) channels, i.e. variance, contrast, dissimilarity, and homogeneity, were also used for the classification. Two classification methods, using Support Vector Machine and Minimum distance were applied for image classification. Landsat 7 ETM images combined with textural information is used for land cover classification of tropical rainforest area. Band 5 of Landsat data was used to compute texture layers using the GLCM and Geostatistics methods. This band was chosen because it has the highest variance of training data compared to other spectral bands. The results were compared to find out how the extra information given by the texture enhances the classification. According to the accuracy assessment using error matrix, combinations of image and texture data performed better with 81% of accuracy compared to those of image data only with 76% of accuracy.
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• Developing forest reference (emission) levels for REDD+ is an urgent and challenging task, given the lack of quality data in many countries, genuine uncertainties about future rates of deforestation and forest degradation and potential... more
• Developing forest reference (emission) levels for REDD+ is an urgent and challenging task, given the lack of quality data in many countries, genuine uncertainties about future rates of deforestation and forest degradation and potential incentives for biasing the estimates.• The availability and quality of data should determine the methods used to develop reference levels.
The development of a system for forest monitoring and measurement, reporting and verification (MRV) is an on-going priority – and challenge – for REDD+ countries. Although many countries already have some form of national forest... more
The development of a system for forest monitoring and measurement, reporting and verification (MRV) is an on-going priority – and challenge – for REDD+ countries. Although many countries already have some form of national forest monitoring in place, the existing capacity often falls short of the level required to participate fully in REDD+. In this context, a group of experts from around the world met in September 2012 to share their experiences and to discuss some of the central – and at times controversial – issues for national forest monitoring readiness and REDD+.
Through the experiences and analyses of five REDD+ countries, two donor organisations and several researchers and negotiators, the papers gathered examine:
- success factors for building capacity and implementing national forest monitoring
- stepwise approaches for bridging capacity gaps through continuous improvement
- key components and attributes of an effective national forest monitoring system
- data and technology needed for forest monitoring
- the conservativeness principle, benefit distribution, and a framework for REDD+ reference levels (stepwise approach)
- assessment of current and required methodological guidance.
By publishing these papers for a wider audience, this collection aims to help all those invested in the success of REDD+ to learn from others’ experiences.
This joint publication of the CIFOR Global Comparative Study on REDD and the GOFC-GOLD Land Cover Office synthesises the main outcomes of that meeting.
Through the experiences and analyses of five REDD+ countries, two donor organisations and several researchers and negotiators, the papers gathered examine:
- success factors for building capacity and implementing national forest monitoring
- stepwise approaches for bridging capacity gaps through continuous improvement
- key components and attributes of an effective national forest monitoring system
- data and technology needed for forest monitoring
- the conservativeness principle, benefit distribution, and a framework for REDD+ reference levels (stepwise approach)
- assessment of current and required methodological guidance.
By publishing these papers for a wider audience, this collection aims to help all those invested in the success of REDD+ to learn from others’ experiences.
This joint publication of the CIFOR Global Comparative Study on REDD and the GOFC-GOLD Land Cover Office synthesises the main outcomes of that meeting.
The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and... more
The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map.
Research Interests: Geomatics, Remote Sensing, Remote Sensing (Earth Sciences), Environmental Remote Sensing, Forest Ecology, and 4 moreRemote sensing and GIS applications in Landscape Research, Remote sensing and GIS, Satellite Remote Sensing (Earth Sciences), and Remote Sensing based forest classification, Spatila database for forestry, forest fires monitoring
Deforestation, mainly caused by illegal logging is a serious problem in Indonesia. Illegal logging is closely related to the quality of forest management, therefore it is one of the main factors that can hinder the sustainability of... more
Deforestation, mainly caused by illegal logging is a serious problem in Indonesia. Illegal logging is closely related to the quality of forest management, therefore it is one of the main factors that can hinder the sustainability of forest resources. This study aimed at the development of method that provides more reliable information of illegal logging in a form of single tree felling by means of multi-stage classification of multi-source data. The study focuses on tropical forest ecosystem in Labanan forest management unit in East Kalimantan Province, Indonesia.
This study explores the possibility of Landsat 7 ETM data acquired in 2003 to detect single tree felling in three stages. In the first stage, seven bands of Landsat image were classified using fuzzy c-means, neural network and maximum likelihood methods. Pixels labelled as single tree felling class by these classifiers were masked out and assigned as ‘first order single tree felling class’. The remaining pixels were defined as ‘unclear single tree felling class’ and classified in the second stage applying neural network method and some ancillary data. The single tree felling class pixels from the second classification was defined as ‘second order single tree felling class’ and the accuracy was assessed using confusion matrices. The first- and second order single tree felling pixels were merged and filtered using expert knowledge-based GIS layers resulting in more reliable illegal logging map.
The results of this study found the fuzzy c-means method has less accuracy than neural network and maximum likelihood techniques for classifying the single tree felling class. In the first classification, the maximum likelihod, fuzzy c-means and neural network methods show 53.3% agreement in discriminating pixels as single tree felling class. Multi-source classification of neural network in the second stage resulted in 80% of accuracy. The best result was found combining multi-spectral bands of Landsat ETM data, aspect, elevation, skewness and variance in the input layer of neural network classifcation. The rule-based classification in the last stage found 8.6 – 15.2 % of total RKL 1 area identified as illegal logging sites.
This study explores the possibility of Landsat 7 ETM data acquired in 2003 to detect single tree felling in three stages. In the first stage, seven bands of Landsat image were classified using fuzzy c-means, neural network and maximum likelihood methods. Pixels labelled as single tree felling class by these classifiers were masked out and assigned as ‘first order single tree felling class’. The remaining pixels were defined as ‘unclear single tree felling class’ and classified in the second stage applying neural network method and some ancillary data. The single tree felling class pixels from the second classification was defined as ‘second order single tree felling class’ and the accuracy was assessed using confusion matrices. The first- and second order single tree felling pixels were merged and filtered using expert knowledge-based GIS layers resulting in more reliable illegal logging map.
The results of this study found the fuzzy c-means method has less accuracy than neural network and maximum likelihood techniques for classifying the single tree felling class. In the first classification, the maximum likelihod, fuzzy c-means and neural network methods show 53.3% agreement in discriminating pixels as single tree felling class. Multi-source classification of neural network in the second stage resulted in 80% of accuracy. The best result was found combining multi-spectral bands of Landsat ETM data, aspect, elevation, skewness and variance in the input layer of neural network classifcation. The rule-based classification in the last stage found 8.6 – 15.2 % of total RKL 1 area identified as illegal logging sites.
Research Interests:
In most conventional classification approach, mutually separation of sample areas from one class to another is required. Mixed pixels are a common problem for satellite data classification and may be found in the classification of low to... more
In most conventional classification approach, mutually separation of sample areas from one class to another is required. Mixed pixels are a common problem for satellite data classification and may be found in the classification of low to medium spatial resolution of the satellite images. The occurrence of mixed pixels causes the uncertainty in class determination which may lead to more miss-classified pixels. Therefore, the class pixels that are considered for the classification need to be adjusted. We use a fuzzy approach to improve the decision of class partitioning over fuzzy sample areas. The purpose of this research is to study the fuzzy approach combined with cluster shape for generating more accurate land cover information.
Factor Analysis combined with Principle Component Analysis (FA-PCA), Optimum Index Factor (OIF), and 6 Bands ALI image are used as the input of this research. Combination of fuzzy classification and cluster shape is applied to training area that already collected. We use Fuzzy C-Means, Fuzzy Shape, and Fuzzy Adjusted for image classification considering sphere and ellipsoid as cluster shape forms. Conventional maximum likelihood classification and Neural Networks method are used as comparison of the fuzzy classification result.
The classification results show that fuzzy approach can be applied as alternative to solve mixed pixel problems in medium spatial resolution images. By adjusting the cluster size in classification could increase accuracy of the result. The fuzzy adjusted with ellipsoid cluster shape and PCA input was the best classification for mixed training areas with the accuracy of up to 82.11%.
Factor Analysis combined with Principle Component Analysis (FA-PCA), Optimum Index Factor (OIF), and 6 Bands ALI image are used as the input of this research. Combination of fuzzy classification and cluster shape is applied to training area that already collected. We use Fuzzy C-Means, Fuzzy Shape, and Fuzzy Adjusted for image classification considering sphere and ellipsoid as cluster shape forms. Conventional maximum likelihood classification and Neural Networks method are used as comparison of the fuzzy classification result.
The classification results show that fuzzy approach can be applied as alternative to solve mixed pixel problems in medium spatial resolution images. By adjusting the cluster size in classification could increase accuracy of the result. The fuzzy adjusted with ellipsoid cluster shape and PCA input was the best classification for mixed training areas with the accuracy of up to 82.11%.
Sumatran tiger population is under serious threat because of habitat fragmentation and possible population isolation as the intensity of alternative land-use increases. Current conservation efforts for the tigers are threatened by... more
Sumatran tiger population is under serious threat because of habitat fragmentation and possible population isolation as the intensity of alternative land-use increases. Current conservation efforts for the tigers are threatened by excessive land-use change, e.g. for commercial and illegal logging, agricultural, settlement, palm oil plantation expansion as well as mining operations. The challenge for conserving this large carnivore is need to be understood to result in the provision of habitat landscape condition which is suitable for the tigers to survive and to successfully reproduce.
This study investigates the habitat suitability for Sumatran tiger (Panthera tigris sumatrae) in Tesso Nillo National Park and surrounding landscape in Sumatra, Indonesia. We derived land use and distance from road networks based on supervised maximum likelihood classification and the interpretation of multispectral Landsat ETM bands with 30m resolution. The stream networks are generated from smoothed SRTM DEM calculating flow direction and flow accumulation of the catchment over the study area. Slope, elevation and aspect are also calculated from the SRTM data. These features are overlaid each other and the value of each grid cell is constructed in a database.
Field work is conducted to collect ground truth data and to study the behavior of the Sumatran tiger and its prey with respect to certain environmental variable. Ultimately, a habitat suitability map for the Sumatran tiger is generated using binomial multiple logistic regression technique integrated with GIS environment during post field work. Based on the suitability map, the biophysical properties and anthropogenic factors affecting the distribution of Sumatran tiger and its prey and the possibility for defining corridor development for the conservation of this species are discussed.
Keywords: Sumatran tiger, land use change, habitat suitability map
This study investigates the habitat suitability for Sumatran tiger (Panthera tigris sumatrae) in Tesso Nillo National Park and surrounding landscape in Sumatra, Indonesia. We derived land use and distance from road networks based on supervised maximum likelihood classification and the interpretation of multispectral Landsat ETM bands with 30m resolution. The stream networks are generated from smoothed SRTM DEM calculating flow direction and flow accumulation of the catchment over the study area. Slope, elevation and aspect are also calculated from the SRTM data. These features are overlaid each other and the value of each grid cell is constructed in a database.
Field work is conducted to collect ground truth data and to study the behavior of the Sumatran tiger and its prey with respect to certain environmental variable. Ultimately, a habitat suitability map for the Sumatran tiger is generated using binomial multiple logistic regression technique integrated with GIS environment during post field work. Based on the suitability map, the biophysical properties and anthropogenic factors affecting the distribution of Sumatran tiger and its prey and the possibility for defining corridor development for the conservation of this species are discussed.
Keywords: Sumatran tiger, land use change, habitat suitability map
Tropical rainforest is the largest ecosystem in the world. This forest has a major role for global carbon cycle. Nowadays, deforestation and forest degradation are of the major problems for sustainability of the tropical rainforest. These... more
Tropical rainforest is the largest ecosystem in the world. This forest has a major role for global carbon cycle. Nowadays, deforestation and forest degradation are of the major problems for sustainability of the tropical rainforest. These problems impact the forest biomass as a source of carbon sink. Disturbance on the forest substantially reduced forest biomass and triggered more carbon released to the atmosphere, altogether these can attribute to the global warming. This work aimed to estimate above ground biomass (AGB) using equation derived from the stand volume prediction and to study spatial distribution of the AGB over a forest area. The potential of remote sensing and field measurement data to predict the stand volume and the AGB were studied.
This study concerned on a tropical rainforest in East Kalimantan, Indonesia. The satellite remote sensing data was atmospherically corrected using Dark Object Subtraction (DOS) technique, and topographic correction was conducted using C-correction method. Stand volume was estimated using field data and remote sensing data, thus using Levenberg-Marquardt algorithm neural network method was employed. Due to unavailability of actual biomass data, the stand volume estimate was converted into the above ground biomass using equations developed for the tropical Amazonian forest assuming similar forest vegetation over both areas. Spatial distribution of the AGB and the error estimate were then interpolated using kriging.
The results showed integration of field measurement and remote sensing data has better prediction of stand volume validated with the actual stand volume data. The AGB estimate showed a great correlation with predicted stand volume data, number of stems, and basal area. Spatial distribution of the AGB described a correlation between forest biomass and land use/land cover in the study area.
Keywords: above ground biomass, stand volume, remote sensing, neural network, kriging
More information about the conference:
http://www.wzw.tum.de/lifo2008//index.php
This study concerned on a tropical rainforest in East Kalimantan, Indonesia. The satellite remote sensing data was atmospherically corrected using Dark Object Subtraction (DOS) technique, and topographic correction was conducted using C-correction method. Stand volume was estimated using field data and remote sensing data, thus using Levenberg-Marquardt algorithm neural network method was employed. Due to unavailability of actual biomass data, the stand volume estimate was converted into the above ground biomass using equations developed for the tropical Amazonian forest assuming similar forest vegetation over both areas. Spatial distribution of the AGB and the error estimate were then interpolated using kriging.
The results showed integration of field measurement and remote sensing data has better prediction of stand volume validated with the actual stand volume data. The AGB estimate showed a great correlation with predicted stand volume data, number of stems, and basal area. Spatial distribution of the AGB described a correlation between forest biomass and land use/land cover in the study area.
Keywords: above ground biomass, stand volume, remote sensing, neural network, kriging
More information about the conference:
http://www.wzw.tum.de/lifo2008//index.php
Timbers are the main product of forests that vastly harvested for commercial purpose. Estimation of timber capacity based on stand volume approach was demonstrated in this study. Linear and nonlinear methods were used to see the... more
Timbers are the main product of forests that vastly harvested for commercial purpose. Estimation of timber capacity based on stand volume approach was demonstrated in this study. Linear and nonlinear methods were used to see the predictive ability of these methods in estimating stand volume. Neural network method trained using Levenberg-Marquardt algorithm was implemented, whereas remote sensing data, vegetation indices and image transform data were applied as predictors. Ordinary kriging was used for interpolating stand volume estimate over the study area. This study found that predictive ability of neural network method outperformed multi-linear regression in estimating the stand volume.
Keywords : stand volume estimate, neural network, Levenberg-Marquardt, Kriging, multi-linear regression
Keywords : stand volume estimate, neural network, Levenberg-Marquardt, Kriging, multi-linear regression