251,510 research outputs found
Monitoring deforestation in Malaysia between 1985 and 2013: Insight from South-Western Sabah and its protected peat swamp area
No full textMonitoring land cover changes provides an effective and accurate evaluation of deforestation rates that shed light on reducing emissions from deforestation and forest degradation (REDD) implementation. Located in Klias Peninsula, southwestern Sabah, Malaysia there lies a pristine peat swamp forest area. This type of ecosystem plays a significant role in global climate regulation. Despite its importance, the peat swamp forest is threatened and highly degraded, due to the increasing demand for agricultural expansion. This is where we monitored deforestation and land cover change between 1985 and 2013. Temporal changes were determined by means of supervised classification, using the maximum likelihood classification rule to observe changes in the area. Post-classification change detection techniques were applied in order to understand the change in forest coverage in the Klias Peninsula and inside the protected area boundaries. The overall accuracy for the Klias Peninsula and the protected area were more than 88% (+/- 4% margin of error) and 95% (+/- 2% margin of error), respectively. Based on these findings, it appears that more than half of the forest area in Klias Peninsula disappeared from 142,713 ha (+/- 6818 margin of error) to 73,403 ha (+/- 6796 margin of error) between 1985 and 2013. The annual rate of change in the protected area was 10.94% (+/- 0.85% margin of error) per year for deforestation and 0.86% (+/- 5.19% margin of error) per year for forest area. The result revealed that most of the peat swamp forest was converted to other stable non-forest areas, including agriculture which can be a threat to the already disturbed protected area. Therefore, we also conducted an accurate monitoring of the forest cover change and deforestation data in the protected area and its surrounding environs in order to promote sound political decision-making regarding the future protection and sustainability of the remaining peat swamp area. (C) 2016 Elsevier Ltd. All rights reserved.Ministry of Higher Education Malaysia [NRGS0005
Deforestation, forest degradation and readiness of local people of Lubuk Antu, Sarawak for REDD+
No full textReducing emissions from deforestation and forest degradation-plus (REDD+) is considered as an important mitigation strategy against global warming. However, the implementation of REDD+ can adversely affect local people who have been practicing shifting cultivation for generations. We analyzed Landsat-5 Thematic Mapper images of 1990 and 2009 to quantifying deforestation and forest degradation at Lubuk Antu District, a typical rural area of Sarawak, Malaysia. The results showed significant loss of intact forest at 0.9% per year, which was substantially higher than the rate of Sarawak. There were increases of oil palm and rubber areas but degraded forest, the second largest land cover type, had increased considerably. The local people were mostly shifting cultivators, who indicated readiness of accepting the REDD+ mechanism if they were given compensation. We estimated the monthly willingness to accept (WTA) at RM462, which can be considered as the opportunity cost of foregoing their existing shifting cultivation. The monthly WTA was well correlated with their monthly household expenses. Instead of cash payment, rubber cultivation scheme was the most preferred form of compensation
Assessing impact of multiple fires on a tropical peat swamp forest using high and very high-resolution satellite images
No full textTropical peat swamp forests, found mainly in Southeast Asia, have been threatened by recurring El Niño fires. Repeated burnings form a complex and heterogeneous landscape comprising a mosaic of burned patches of different fire frequencies, requiring fine-scale assessment to understand their impact. We examined the impact of the El Niño fires of 1998 and 2003 on a tropical peat swamp forest in northern Borneo, with the combined use of high and very high-resolution satellite images. Object-based and pixel-based classifications were compared to classify a QuickBird image. Burned patches of different fire frequencies were derived based on unsupervised classification of the principal components of multitemporal Normalized Difference Water Index (NDWI) data. The results show that the object-based classification was more accurate than the pixel-based classification for generating a detailed land cover map. Fire frequency had a severe impact on the number of burned patches and the residual forest cover. Larger patch area retained more residual forest cover for the burned patches. Forest structure of burned-twice patches was more severely altered compared to burned-once patches. Two burned-once patches had a relatively promising recovery potential by natural regeneration due to higher residual forest cover, a vast number of large trees, and aboveground biomass. Except for the largest patch, rehabilitation seemed inevitable for burned-twice patches. This approach can be applied to assess the impact of multiple fires on other forest types for better post-fire forest management
A GIS-based multi-criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia
No full textEvaluation of environmental functions of tropical forest in Kinabalu Park, Sabah, Malaysia using GIS and remote sensing techniques: Implications to forest conservation planning
No full textEnvironmental functions of tropical forest can serve as criteria for forest conservation planning in the tropics. The objective of this study is to evaluate the environmental functions of tropical forest in Kinabalu Park, Sabah, Malaysia, using GIS and remote sensing techniques. Field data, statistical data, including weather data with geographic localities, maps and satellite image are collected. Linear regression models are developed for forests of different geological substrates, based on the relationships between altitude and biodiversity (Fisher’s alpha index). Biodiversity conservation function map is derived with the statistical models and a digital elevation model. Coupling with extensive literature review, an evaluation matrix for evaluating soil and water conservation functions including landslide prevention, flood prevention and drought prevention functions, is constructed. To evaluate the soil and water conservation functions, a weighted linear combination method is used with GIS layers of topography, geology, soil depth, rainfall and slope. Forest areas in Kinabalu Park are derived with land cover mapping using Landsat-TM image. Areas having high values of biodiversity conservation, flood and drought prevention functions are covered with mainly lowland rain forest. On the other hand, areas with high values of the landslide prevention function are covered with mainly subalpine forests. Using the environmental functions, a conservation index is computed to represent forests that are important to conservation. Based on theCI, the lowland rain forest receives highest priority in protection. In fact, it is located in the boundary areas of the park and thus exposed to illegal activities
Deforestation detection in Kinabalu Area, Sabah, Malaysia by using multi-sensor remote sensing approach
No full textThis paper examines use of multi-sensor remote sensing approach for deforestation detection in the tropics. Multi-sensor satellite data of Landsat-MSS of 1973 and Landsat-TM of 1991 and 1996 were employed. Accuracy of image-to-image registration was below 1 pixel. Relative radiometric normalization of Landat-MSS 1973 and Landsat-TM 1991 to Landsat-TM 1996 as the reference image was carried out to remove the unwanted variabilities between all the satellite images. Image differencing algorithm with Normalized Difference Vegetation Index (NDVI) was examined for deforestation detection. The performance of the NDVI image differencing algorithm for deforestation detection between 1973 and 1996 was investigated at three test sites covered with reliable ground truths. The accuracy of detection was satisfactory that the algorithm was used in deforestation detection of the whole study area in two change periods i.e. I: 1973-1991 and II: 1991-1996. Although false deforestation pixels in period I were also detected, it can easily be rectified using a land use map of 1984. In total, 2,445ha of forest, which is almost 1% of the study area, were cleared from 1973-1996 and most of them were deforested in period I (2,090ha). This study concludes that the multi-sensor approach is a useful solution for deforestation detection because of better temporal coverage. It can also provide more satellite data for the application and thus lessen data acquisition problem due to cloud cover which is a consistent problem for the tropics
Modeling the Natural Occurrence of Selected Dipterocarp Genera in Sarawak, Borneo
No full textDipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed
Estimation of stand volume of conifer forest: A Bayesian approach based on satellite-based estimate and forest register data
No full textThis paper highlights some problems underlying the gathering of stand volume information by forest register and the estimation of stand volume using satellite data. It was found that volume information from the forest register (Vreg) underestimates the actual stand volume. Satellite data is a promising source for estimating stand volume on a large‐area basis, but stand volume estimation remote sensing still suffers from the problem of large variations due to unwanted noise. We present a Bayesian approach that combines stand volume estimates from remotely sensed data (Landsat‐TM) and forest register. Pure Sugi (Cryptomeria japonica D.Don) forest stands were delineated using orthophotographs and forest information such as compartments, sub‐compartments and roads from a Geographic Information System (GIS) database. Pattern decomposition method (PDM) was used to derive remote sensing indices (vegetation, soil, and water indices), which were used for estimating stand age. Stand volume was indirectly estimated from remotely‐sensed data (VRS) through the use of stand age. A Bayesian estimate of stand volume by age class (VB) was performed by assuming the Vreg and VRS as a priori probability distribution and random sample distribution, respectively. The Bayesian approach was found to improve the volume estimate from the forest register. In this way, the widely available forest register can be effectively used together with remote sensing data for estimating stand volume, an approach that is potentially useful for monitoring stand volume on a regional or national scale
Estimating mangrove above-ground biomass in Sabah, Malaysia using field measurements, shuttle radar topography mission and landsat data
No full textMangroves are one of the most productive forest ecosystems and play an important role in carbon storage. We examined the use of Shuttle Radar Topography Mission (SRTM) data to estimate mangrove Above-ground Biomass (AGB) in Sabah, Malaysia. SRTM-DEM can be considered as Canopy Height Model (CHM) because of the flat coastal topography. Nevertheless, we also introduced ground elevation correction using a Digital Terrain Model (DTM) generated with GIS and coastal profile data. We mapped the mangrove forest cover using Landsat imagery acquired in 2015 with the supervised classification method (Kappa coefficient of 0.81). Regression analyses of field AGB and the CHMs resulted in an estimation model with the corrected CHM as the best predictor (R2: 0.73) and cross-validated Root Mean Square Error (RMSE) was 19.70 Mg ha-1 (RMSE%: 11.60). Our study showed Sabah has a mangrove cover of 268,631.91 ha with a total AGB of 44,163,207.07 Mg in 2015. This substantial amount of carbon storage should be monitored over time and managed as part of the climate change mitigation strategy
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