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Estimation of Tropical Forest Structural Characteristics Using ALOS-2 SAR Data 被引量:1
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作者 Luong Viet Nguyen Ryutaro Tateishi +3 位作者 Hoan Thanh Nguyen ram c. sharma Tu Trong To Son Mai Le 《Advances in Remote Sensing》 2016年第2期131-144,共14页
The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data... The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass. 展开更多
关键词 Forest Structure Forest Biomass SAR ALOS-2 Backscattering Intensity Sensitivity Analysis
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Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula
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作者 Nguyen Thanh Hoan ram c. sharma +1 位作者 Nguyen Van Dung Dang Xuan Tung 《Journal of Geoscience and Environment Protection》 2020年第9期24-32,共9页
The Indochinese Peninsula, which contains two thirds of the world’s tropical forests, however, is one of the world’s most threatened habitat with some of the highest rates of deforestation and land use changes. Avai... The Indochinese Peninsula, which contains two thirds of the world’s tropical forests, however, is one of the world’s most threatened habitat with some of the highest rates of deforestation and land use changes. Availability of higher resolution satellite data collected by the likes of Landsat 8 and Sentinel-1-2 has brought new opportunities for precise land cover monitoring in recent years. However, utilizing a massive volume of high spatial and temporal resolution data for ecological applications is challenging. One approach is to employ composite images generated from the multi-temporal satellite data. The research was conducted in two study sites located in the Indochinese Peninsula, Laos-Thailand and Vietnam-Cambodia, vulnerable to deforestation and land use changes. We assessed the potential of recently available composite images, such as Biophysical Image Composite (BIC), Forest Cover Composite (FCC), Enhanced Forest Cover Composite (EFCC), and Water Cover Composite (WCC) for the classification and mapping of land cover types. Three machine learning classifiers, k-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Random Forests (RF) were employed and the performance of composite images was evaluated quantitatively with the support of ground truth data. The overall accuracies (Kappa coefficient) obtained from the combination of composite images were 0.92 (0.89) and 0.90 (0.86) for Laos-Thailand, and Vietnam sites respectively. These results highlight effectiveness of the composite images for the classification and mapping of land cover types. 展开更多
关键词 Satellite Remote Sensing Machine Learning Optical RADAR
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Spectral Features for the Detection of Land Cover Changes
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作者 ram c. sharma Hoan Thanh Nguyen +3 位作者 Saeid Gharechelou Xiulian Bai Luong Viet Nguyen Ryutaro Tateishi 《Journal of Geoscience and Environment Protection》 2019年第5期81-93,共13页
Derivation of more sensitive spectral features from the satellite data is immensely important for better retrieving land cover information and change monitoring, such as changes in snow covered area, forests, and barr... Derivation of more sensitive spectral features from the satellite data is immensely important for better retrieving land cover information and change monitoring, such as changes in snow covered area, forests, and barren lands as some examples from local to the global scale. The major objectives of this paper are to present the potential of water-resistant snow index (WSI) for the detection of snow cover changes in the Himalayas, extant two composite images, biophysical image composite (BIC) and forest cover composite (FCC) for the detection of changes in barren lands and forested areas respectively, and two newly designed composite images, water cover composite (WCC) and urban cover composite (UCC) for the detection of changes in water and urban areas respectively. This research implemented the image compositing technique for the detection and visualization of land cover changes (water, forest, barren, and urban) with respect to local administrative areas where a significant land cover change occurred from 2001 to 2016. A case study was also conducted in the Himalayan region to identify snow cover changes from 2001 to 2015 using the WSI. Analysis of the annual variation of the snow cover in the Himalayas indicated a decreasing trend of the snow cover. Consequently, the downstream areas are more likely to suffer from snow related hazards such as glacial outbursts, avalanches, landslides and floods. The changes in snow cover in the Himalayas may bring significant hydrophysical and livelihood changes in the downstream area including the Mekong Delta. Therefore, the countries sharing the Himalayan region should focus on adapting the severe impacts of snow cover changes. The image compositing approach presented in the research demonstrated promising performance for the detection and visualization of other land cover changes as well. 展开更多
关键词 Land COVER Water COVER COMPOSITE Urban COVER COMPOSITE SNOW COVER COMPOSITE BIOPHYSICAL Image COMPOSITE Forest COVER COMPOSITE HIMALAYAS MODIS Change DETECTION
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Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product
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作者 ram c. sharma Keitarou Hara +10 位作者 Hidetake Hirayama Ippei Harada Daisuke Hasegawa Mizuki Tomita Jong Geol Park Ichio Asanuma Kevin M. Short Masatoshi Hara Yoshihiko Hirabuki Michiro Fujihara Ryutaro Tateishi 《Advances in Remote Sensing》 2017年第1期54-65,共12页
Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an... Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data. 展开更多
关键词 VEGETATION PHYSIOGNOMY MULTI-SPECTRAL TOPOGRAPHY MODIS Mapping Machine Learning Japan MCD12Q1
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Improvement of Countrywide Vegetation Mapping over Japan and Comparison to Existing Maps
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作者 ram c. sharma Keitarou Hara Hidetake Hirayama 《Advances in Remote Sensing》 2018年第3期163-170,共8页
This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data. The Nadir BRDF-Adjusted Reflectance (MCD43A4... This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data. The Nadir BRDF-Adjusted Reflectance (MCD43A4 product) data were compared to the conventional Surface Reflectance (MOD09A1/MOY09A1 products) data for the classification of vegetation types: evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, herbaceous, arable;and non-vegetation. Very rich spectral and temporal features were prepared from MCD43A4 and MOD09A1/MOY09A1 products. Random Forests classifier was employed for the classification of vegetation types with the support of ground truth data prepared in the research. Accuracy metrics—confusion matrix, overall accuracy, and kappa coefficient calculated through 10-fold cross-validation approach—were used for quantitative comparison of MCD43A4 and MOD09A1/MOY09A1 products. The cross-validation results indicated better performance of the MCD43A4 (Overall accuracy = 0.73;Kappa coefficient = 0.69) product than conventional MOD09A1/MOY09A1 products (Overall accuracy = 0.70;Kappa coefficient = 0.66) for the classification. McNemar’s test was also used to confirm a significant difference (p-value = 0.0003) between MCD43A4 and MOD09A1/MOY09A1 products. Based on these results, by utilizing the MCD43A4 features, a new vegetation map was produced for all of Japan. The newly produced map showed better accuracy than the extant, MODIS Land Cover Type product (MCD12Q1) and Global Land Cover by National Mapping Organizations (GLCNMO) product in Japan. 展开更多
关键词 VEGETATION Classification MODIS RANDOM FORESTS Mapping JAPAN
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