arief wijaya

Mangroves are unique tropical coastal wetland ecosystems that have important roles to climate change adaptation and mitigation. Recent studies showed that mangroves have been severely threatened by deforestation, land use change, aquaculture and other human activities. This study shows that remote sensing techniques can assess the spatial extent and above-ground biomass spatial data of mangrove ecosystems in Segara Anakan lagoon, southern coast of Central Java Province. By combining Landsat 7 ETM+ satellite and above-ground biomass field data in 2009, there was increasing pattern of above-ground biomass with mean value of 359 Mg ha−1 and 547 Mg ha−1 for 2001 and 2013, respectively. The increasing biomass value implies that mangrove ecosystems in this lagoon could survive and grew well, although severely threatened due to mangrove conversion. Despite of some limitation data availability and processes, this study fills the gap information on above-ground biomass mapping and may be very valuable to support monitoring on deforestation and degradation and other activities like measuring, reporting, and verification (MRV) of carbon stocks and reducing emission from deforestation and degradation (REDD+) especially on coastal mangrove forests.

"REDD+ measuring, reporting and verification – science solutions to policy challenges 10–12 June 2013, Zeist, The Netherlands A workshop entitled ‘REDD+ measuring, reporting and verification – science solutions to policy challenges’ was organized by the WWF Forest and Climate Initiative, WWF Netherlands and Wageningen University REDD@WUR network from 10th to 12th June 2013 in Zeist, The Netherlands. The purpose of this workshop was to assess the status and development of monitoring approaches in light of the evowlving REDD+ measuring, reporting and verification needs from different actors in the REDD+ measuring, reporting and verification process. Accordingly, the most important gaps were identified and led to the development of research priorities with focus on better linking local and national REDD+ efforts on five themes, namely: monitoring and measurement; reporting and verification; reference levels; measuring, reporting and verification of safeguards; and benefit sharing."

ABSTRACT Developing countries participating in the mitigation mechanism of reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks (REDD+), need to determine a national forest reference emission level (REL) as part of their national monitoring system, which serves as a benchmark to measure the impact of their REDD+ actions. Using data from Indonesia, we show that the choice of a forest definition can have a large impact on estimates of deforestation and forest degradation areas, on assessment of drivers of deforestation and on the development of a REL. The total area of deforestation between 2000 and 2009 was 4.9 million ha when using the FAO definition, 18% higher when using a ‘natural forest definition’ and 27% higher when using the national definition. Using the national and natural forest definitions, large areas (>50%) were classified as shrubland after deforestation. We used regression models to predict future deforestation. Deforestation was much better predicted than degradation (R2 of 0.81 vs. 0.52), with the natural forest definition giving the best prediction. Apart from historical deforestation and initial forest cover, gross domestic product and human population were important predictors of future deforestation in Indonesia. Degradation processes were less well modeled and predictions relied on estimates of historical degradation and forest cover.

Forest cover mapping is necessary to monitor forest cover changes in order to support sustainable forest management. One of the most important factors that cause deforestation comes from Illegal logging. Illegal loggers were cutting trees selectively, based on tree-diameter and tree-species. Remote sensing is a promising tool, which can be used for detecting this type of logging and deforestation the tropical forest. This study applied two different soft classification techniques, ie fuzzy c-means classification and neural network ...

Forest inventory is a renewable resource that severely depleted due to excessive human activities, such as timber harvesting, expansion of settlement and agricultural areas, forest fire, and other types of forest encroachment. Nowadays, sustainable forest management becomes a global issue since changes of forest cover; especially those of tropical forests affect global climate change significantly. Many studies have been conducted in order to mapping forest cover change within certain period of time using different ...

Summary/Kata Kunci: Boiling and soaking times of tempeh production processes are two factors influencing the quality of soybean fermentation of tempeh fungi (Rhizopus ologosporus sp.). An optimal production processes period would help reducing production costs and improve the quality of tempeh fungi. This study aimed at designing and applying fuzzy logic technique in optimizing boiling and soaking times to produce the top amount of tempeh product. Combination of boiling and soaking times of tempeh were used for ...

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.

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 ...

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 ...

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 (ie optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is ...

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 ...

The Main Ethiopian Rift (MER) which is characterized by large elevation differences and occurrence of Precambrian basement rocks and post-Miocene volcanic rocks is severely affected by wide gullies and dynamically expanding into agricultural lands at an alarming rate. We study ...

The Main Ethiopian Rift (MER) is an area of extreme topography underlain by post-Miocene volcanic rocks, Jurassic limestone and a Precambrian basement. A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land. We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data and geomorphologic parameters to discern patterns and features of gully erosion in the MER. Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) classifiers are used to extract different gully shapes and patterns. Several spatial textures based on Grey Level Co-occurrence Matrices (GLCMs) are then generated. Afterwards, the same classifiers are applied to the ASTER data combined with the spatial texture information. We used geomorphologic parameters extracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes. The classifications show accuracies varying between 67% and 89%. Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening. This study reveals the utility of combining ASTER data and spatial textural information in discerning areas affected by gully erosion.