Different land cover classification methods exist. This paper shows that Support vector machines ... more Different land cover classification methods exist. This paper shows that Support vector machines (SVMs) have the potential to outperform Maximum Likelihood and Minimum Distance Classification in rugged area covered with rainforest and in undulating areas dominated by fields, grassland and coniferous forest. However, SVM does not guarentee better classification results. Kernel and generalization parameters as well as the input variables significantly influence the performance of SVM.
This study demonstrates the utility of mosaic ALOS PALSAR data for the estimation of above ground... more This study demonstrates the utility of mosaic ALOS PALSAR data for the estimation of above ground biomass and stem volume in tropical lowland forest of Kalimantan, Central Indonesia. The HH, HV and HV/HH polarizations were correlated with the forest properties using polynomial empirical models. The HV and HV/HH polarizations were best fitted for estimating the AGB and stem volume, respectively. The results showed that mosaic ALOS PALSAR data may be used as initial predictions of the AGB and stem volume, and the proposed approach have potential to apply for global datasets.
Tropical rainforests are the largest ecosystems in the world and have a major role for global car... more Tropical rainforests are the largest ecosystems in the world and have a major role for global carbon cycle, as forest biomass is a carbon sink. This work aimed at estimating above ground biomass (AGB) of a tropical rainforest in Kalimantan, Central Indonesia using a non-destructive approach of remote sensing.
Timbers are the main product of forests that vastly harvested for commercial purpose. Estimation ... 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.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
The capability of L-band radar backscatter to penetrate through the forest canopy is useful for m... more The capability of L-band radar backscatter to penetrate through the forest canopy is useful for mapping the forest structure, including above ground biomass (AGB) estimation. Recent studies confirmed that the empirical AGB models generated from the L-band radar backscatter can provide favourable estimation results, especially if the data has dual-polarization configuration. Using dual polarimetry SAR data the backscatter signal is more sensitive to forest biomass and forest structure because of tree trunk scattering, thus showing better discriminations of different forest successional stages. These SAR approaches, however, need to be further studied for the application in tropical peatlands ecosystem We aims at estimating forest carbon stocks and stand biophysical properties using combination of multi-temporal and multi-polarizations (quad-polarimetric) L-band SAR data and focuses on tropical peat swamp forest over Kampar Peninsula at Riau Province, Sumatra, Indonesia which is one of the most peat abundant region in the country. Applying radar backscattering (Sigma nought) to model the biomass we found that co-polarizations (HH and VV) band are more sensitive than cross-polarization channels (HV and VH). Individual HH polarization channel from April 2010 explained > 86% of AGB. Whereas VV polarization showed strong correlation coefficients with LAI, tree height, tree diameter and basal area. Surprisingly, polarimetric anisotropy feature from April 2007 SAR data show relatively high correlations with almost all forest biophysical parameters. Polarimetric anisotropy, which explains the ratio between the second and the first dominant scattering mechanism from a target has reduced at some extent the randomness of scattering mechanism, thus improve the predictability of this particular feature in estimating the forest properties. These results may be influenced by local seasonal variations of the forest as well as moisture, but available quad-pol SAR data were unable to show these patterns, since all the SAR data were acquired during the rainy season. The results of multi-regression analysis in predicting above ground biomass shows that ALOS PALSAR data acquired in 2010 has outperformed other time series data. This is probably due to the fact that land cover change in the area from 2007 -2009 was highly dynamic, converting natural forests into rubber and Acacia plantations, thus SAR data of 2010 which was acquired in between of two field campaigns has provided significant results (F = 40.7, P < 0.005). In general, we found that polarimetric features have improved the models performance in estimating AGB. Surprising results come from single HH polarization band from April 2010 that has a strong correlation with AGB (r = 0.863). Also, HH polarization band of 2009 SAR image resulted in a moderate correlation with AGB (r = 0.440).
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
THEME: Forests, Biodiversity and Terrestrial Ecosystems (BIOD theme). This is an invited presenta... more THEME: Forests, Biodiversity and Terrestrial Ecosystems (BIOD theme). This is an invited presentation to be presented at the special session of "Trends in operational land cover mapping", which has been approved by the Technical Program Committee and the chairs of the sessions are Konrad Wessels (CSIR, South Africa) and Brice Mora (GOFC-GOLD
Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, det... more Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, determines species composition and influences landscape structure. However, wildfires may: have undesirable effects on biodiversity and vegetation coverage; produce carbon emissions to the atmosphere; release smoke affecting human health; and cause loss of lives and property. There have been increasing concerns about the potential impacts of climate variability and change on forest fires. Climate change can alter factors that influence the occurrence of fire ignitions, fuel availability and fuel flammability. This review paper aims to identify tools and methods used for gathering information about the impacts of climate variability and change on forest fires, forest fuels and the probability of fires. Tools to assess the impacts of climate variability and change on forest fires include: remote sensing, dynamic global vegetation and landscape models, integrated fire-vegetation models, fire danger rating systems, empirical models and fire behavior models. This review outlines each tool in terms of its characteristics, spatial and temporal resolution, limitations and applicability of the results. To enhance and improve tool performance, each must be continuously tested in all types of forest ecosystems.
Different land cover classification methods exist. This paper shows that Support vector machines ... more Different land cover classification methods exist. This paper shows that Support vector machines (SVMs) have the potential to outperform Maximum Likelihood and Minimum Distance Classification in rugged area covered with rainforest and in undulating areas dominated by fields, grassland and coniferous forest. However, SVM does not guarentee better classification results. Kernel and generalization parameters as well as the input variables significantly influence the performance of SVM.
This study demonstrates the utility of mosaic ALOS PALSAR data for the estimation of above ground... more This study demonstrates the utility of mosaic ALOS PALSAR data for the estimation of above ground biomass and stem volume in tropical lowland forest of Kalimantan, Central Indonesia. The HH, HV and HV/HH polarizations were correlated with the forest properties using polynomial empirical models. The HV and HV/HH polarizations were best fitted for estimating the AGB and stem volume, respectively. The results showed that mosaic ALOS PALSAR data may be used as initial predictions of the AGB and stem volume, and the proposed approach have potential to apply for global datasets.
Tropical rainforests are the largest ecosystems in the world and have a major role for global car... more Tropical rainforests are the largest ecosystems in the world and have a major role for global carbon cycle, as forest biomass is a carbon sink. This work aimed at estimating above ground biomass (AGB) of a tropical rainforest in Kalimantan, Central Indonesia using a non-destructive approach of remote sensing.
Timbers are the main product of forests that vastly harvested for commercial purpose. Estimation ... 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.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
The capability of L-band radar backscatter to penetrate through the forest canopy is useful for m... more The capability of L-band radar backscatter to penetrate through the forest canopy is useful for mapping the forest structure, including above ground biomass (AGB) estimation. Recent studies confirmed that the empirical AGB models generated from the L-band radar backscatter can provide favourable estimation results, especially if the data has dual-polarization configuration. Using dual polarimetry SAR data the backscatter signal is more sensitive to forest biomass and forest structure because of tree trunk scattering, thus showing better discriminations of different forest successional stages. These SAR approaches, however, need to be further studied for the application in tropical peatlands ecosystem We aims at estimating forest carbon stocks and stand biophysical properties using combination of multi-temporal and multi-polarizations (quad-polarimetric) L-band SAR data and focuses on tropical peat swamp forest over Kampar Peninsula at Riau Province, Sumatra, Indonesia which is one of the most peat abundant region in the country. Applying radar backscattering (Sigma nought) to model the biomass we found that co-polarizations (HH and VV) band are more sensitive than cross-polarization channels (HV and VH). Individual HH polarization channel from April 2010 explained > 86% of AGB. Whereas VV polarization showed strong correlation coefficients with LAI, tree height, tree diameter and basal area. Surprisingly, polarimetric anisotropy feature from April 2007 SAR data show relatively high correlations with almost all forest biophysical parameters. Polarimetric anisotropy, which explains the ratio between the second and the first dominant scattering mechanism from a target has reduced at some extent the randomness of scattering mechanism, thus improve the predictability of this particular feature in estimating the forest properties. These results may be influenced by local seasonal variations of the forest as well as moisture, but available quad-pol SAR data were unable to show these patterns, since all the SAR data were acquired during the rainy season. The results of multi-regression analysis in predicting above ground biomass shows that ALOS PALSAR data acquired in 2010 has outperformed other time series data. This is probably due to the fact that land cover change in the area from 2007 -2009 was highly dynamic, converting natural forests into rubber and Acacia plantations, thus SAR data of 2010 which was acquired in between of two field campaigns has provided significant results (F = 40.7, P < 0.005). In general, we found that polarimetric features have improved the models performance in estimating AGB. Surprising results come from single HH polarization band from April 2010 that has a strong correlation with AGB (r = 0.863). Also, HH polarization band of 2009 SAR image resulted in a moderate correlation with AGB (r = 0.440).
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
THEME: Forests, Biodiversity and Terrestrial Ecosystems (BIOD theme). This is an invited presenta... more THEME: Forests, Biodiversity and Terrestrial Ecosystems (BIOD theme). This is an invited presentation to be presented at the special session of "Trends in operational land cover mapping", which has been approved by the Technical Program Committee and the chairs of the sessions are Konrad Wessels (CSIR, South Africa) and Brice Mora (GOFC-GOLD
Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, det... more Fire is an intrinsic element of many forest ecosystems; it shapes their ecological processes, determines species composition and influences landscape structure. However, wildfires may: have undesirable effects on biodiversity and vegetation coverage; produce carbon emissions to the atmosphere; release smoke affecting human health; and cause loss of lives and property. There have been increasing concerns about the potential impacts of climate variability and change on forest fires. Climate change can alter factors that influence the occurrence of fire ignitions, fuel availability and fuel flammability. This review paper aims to identify tools and methods used for gathering information about the impacts of climate variability and change on forest fires, forest fuels and the probability of fires. Tools to assess the impacts of climate variability and change on forest fires include: remote sensing, dynamic global vegetation and landscape models, integrated fire-vegetation models, fire danger rating systems, empirical models and fire behavior models. This review outlines each tool in terms of its characteristics, spatial and temporal resolution, limitations and applicability of the results. To enhance and improve tool performance, each must be continuously tested in all types of forest ecosystems.
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