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Utilization of the Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above-ground biomass was assessed in the New England region. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6)-based BRDF parameters. The effects of the seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on the above-ground biomass, the angular relationship of the spectral reflectance with above-ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI and Hot-spot incorporated NDVI showed significant relationship (more than 50%) with the above-ground biomass. The Vegetation Structure Index (VSI), newly proposed in the research, performed in the most efficient way and explained 64% variation of the above-ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above-ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass, while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.
Ram Sharma. Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-Angular Satellite Remote Sensing. Journal of Imaging 2021, 7, 84 .
AMA StyleRam Sharma. Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-Angular Satellite Remote Sensing. Journal of Imaging. 2021; 7 (5):84.
Chicago/Turabian StyleRam Sharma. 2021. "Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-Angular Satellite Remote Sensing." Journal of Imaging 7, no. 5: 84.
Vegetation mapping and monitoring is important as the composition and distribution of vegetation has been greatly influenced by land use change and the interaction of land use change and climate change. The purpose of vegetation mapping is to discover the extent and distribution of plant communities within a geographical area of interest. The paper introduces the Genus-Physiognomy-Ecosystem (GPE) system for the organization of plant communities from the perspective of satellite remote sensing. It was conceived for broadscale operational vegetation mapping by organizing plant communities according to shared genus and physiognomy/ecosystem inferences, and it offers an intermediate level between the physiognomy/ecosystem and dominant species for the organization of plant communities. A machine learning and cross-validation approach was employed by utilizing multi-temporal Landsat 8 satellite images on a regional scale for the classification of plant communities at three hierarchical levels: (i) physiognomy, (ii) GPE, and (iii) dominant species. The classification at the dominant species level showed many misclassifications and undermined its application for broadscale operational mapping, whereas the GPE system was able to lessen the complexities associated with the dominant species level classification while still being capable of distinguishing a wider variety of plant communities. The GPE system therefore provides an easy-to-understand approach for the operational mapping of plant communities, particularly on a broad scale.
Ram Sharma. Genus-Physiognomy-Ecosystem (GPE) System for Satellite-Based Classification of Plant Communities. Ecologies 2021, 2, 203 -213.
AMA StyleRam Sharma. Genus-Physiognomy-Ecosystem (GPE) System for Satellite-Based Classification of Plant Communities. Ecologies. 2021; 2 (2):203-213.
Chicago/Turabian StyleRam Sharma. 2021. "Genus-Physiognomy-Ecosystem (GPE) System for Satellite-Based Classification of Plant Communities." Ecologies 2, no. 2: 203-213.
Utilization of Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above ground biomass was assessed. This research was implemented in the New England region with the availability of a high quality forest inventory database. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6) based BRDF parameters. The effects of seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on above ground biomass, angular relationship of the spectral reflectance with above ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI ( and Hot-spot incorporated NDVI ( showed significant relationship (more than 50%) with the above ground biomass. This research proposed two more sensitive vegetation structural indices, Fore-scattering Back-scattering NDVI and Vegetation Structure Index (VSI). The Fore-scattering Back-scattering NDVI showed higher sensitivity (R2 = 0.62, RMSE = 52.46) towards the above ground biomass than existing multi-angular vegetation indices. Furthermore, the VSI performed in the most efficient way explaining 64% variation of the above ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.
Ram C. Sharma. Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-angular Satellite Remote Sensing. 2021, 1 .
AMA StyleRam C. Sharma. Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-angular Satellite Remote Sensing. . 2021; ():1.
Chicago/Turabian StyleRam C. Sharma. 2021. "Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-angular Satellite Remote Sensing." , no. : 1.
This paper presents ensemble learning of multi-source satellite sensors dataset to obtain better predictive performance of the forest biomass. Spectral, spectral-indices, and spectral-textural features were generated from two optical satellite sensors, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). In addition, two radar satellite sensors, Sentinel-1 C-band Synthetic Aperture Radar (CSAR), and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) were utilized to generate backscattering and backscattering-textural features. The plot-wise above ground biomass data available from five forests in New England region were utilized. Ensemble learning of multi-source satellite sensors dataset was carried out by employing four machine learning regressors namely, Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and Multilayer Perceptron (MLP). A five-fold cross-validation method was used to evaluate predictive performance of the multi-source satellite sensors. The integration of multi-source satellite features, comprising of spectral, spectral-indices, backscattering, spectral-textural, and backscattering-textural information, through ensemble learning and cross-validation approach implemented in the research showed promising results (R2 = 0.81, RMSE = 46.2 Mg/ha) for the estimation of plots-level forest biomass in New England region.
Ram C. Sharma. Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region. 2021, 1 .
AMA StyleRam C. Sharma. Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region. . 2021; ():1.
Chicago/Turabian StyleRam C. Sharma. 2021. "Ensemble Learning of Multi-Source Satellite Sensors Dataset for Estimating Forest Biomass in New England Region." , no. : 1.
Vegetation indices are commonly used techniques for the retrieval of biophysical and chemical attributes of vegetation. This paper presents the potential of an Autoencoders (AEs) and Convolutional Autoencoders (CAEs)-based self-supervised learning approach for the decorrelation and dimensionality reduction of high-dimensional vegetation indices derived from satellite observations. This research was implemented in Mt. Zao and its base in northeast Japan with a cool temperate climate by collecting the ground truth points belonging to 16 vegetation types (including some non-vegetation classes) in 2018. Monthly median composites of 16 vegetation indices were generated by processing all Sentinel-2 scenes available for the study area from 2017 to 2019. The performance of AEs and CAEs-based compressed images for the clustering and visualization of vegetation types was quantitatively assessed by computing the bootstrap resampling-based confidence interval. The AEs and CAEs-based compressed images with three features showed around 4% and 9% improvements in the confidence intervals respectively over the classical method. CAEs using convolutional neural networks showed better feature extraction and dimensionality reduction capacity than the AEs. The class-wise performance analysis also showed the superiority of the CAEs. This research highlights the potential of AEs and CAEs for attaining a fine clustering and visualization of vegetation types.
Ram Sharma; Keitarou Hara. Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types. Journal of Imaging 2021, 7, 30 .
AMA StyleRam Sharma, Keitarou Hara. Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types. Journal of Imaging. 2021; 7 (2):30.
Chicago/Turabian StyleRam Sharma; Keitarou Hara. 2021. "Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types." Journal of Imaging 7, no. 2: 30.
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.
Nguyen Thanh Hoan; Ram C. Sharma; Nguyen Van Dung; Dang Xuan Tung. Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula. Journal of Geoscience and Environment Protection 2020, 08, 24 -32.
AMA StyleNguyen Thanh Hoan, Ram C. Sharma, Nguyen Van Dung, Dang Xuan Tung. Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula. Journal of Geoscience and Environment Protection. 2020; 08 (09):24-32.
Chicago/Turabian StyleNguyen Thanh Hoan; Ram C. Sharma; Nguyen Van Dung; Dang Xuan Tung. 2020. "Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula." Journal of Geoscience and Environment Protection 08, no. 09: 24-32.
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.
Ram C. Sharma; Hoan Thanh Nguyen; Saeid Gharechelou; Xiulian Bai; Luong Viet Nguyen; Ryutaro Tateishi. Spectral Features for the Detection of Land Cover Changes. Journal of Geoscience and Environment Protection 2019, 07, 81 -93.
AMA StyleRam C. Sharma, Hoan Thanh Nguyen, Saeid Gharechelou, Xiulian Bai, Luong Viet Nguyen, Ryutaro Tateishi. Spectral Features for the Detection of Land Cover Changes. Journal of Geoscience and Environment Protection. 2019; 07 (05):81-93.
Chicago/Turabian StyleRam C. Sharma; Hoan Thanh Nguyen; Saeid Gharechelou; Xiulian Bai; Luong Viet Nguyen; Ryutaro Tateishi. 2019. "Spectral Features for the Detection of Land Cover Changes." Journal of Geoscience and Environment Protection 07, no. 05: 81-93.
Hanoi City of Vietnam changes quickly, especially after its state implemented its Master Plan 2030 for the city’s sustainable development in 2011. Then, a number of environmental issues are brought up in response to the master plan’s implementation. Among the issues, the Urban Heat Island (UHI) effect that tends to cause negative impacts on people’s heath becomes one major problem for exploitation to seek for mitigation solutions. In this paper, we investigate the land surface thermal signatures among different land-use types in Hanoi. The surface UHI (SUHI) that characterizes the consequences of the UHI effect is also studied and quantified. Note that our SUHI is defined as the magnitude of temperature differentials between any two land-use types (a more general way than that typically proposed in the literature), including urban and suburban. Relationships between main land-use types in terms of composition, percentage coverage, surface temperature, and SUHI in inner Hanoi in the recent two years 2016 and 2017, were proposed and examined. High correlations were found between the percentage coverage of the land-use types and the land surface temperature (LST). Then, a regression model for estimating the intensity of SUHI from the Landsat 8 imagery was derived, through analyzing the correlation between land-use composition and LST for the year 2017. The model was validated successfully for the prediction of the SUHI for another hot day in 2016. For example, the transformation of a chosen area of 161 ha (1.61 km2) from vegetation to built-up between two years, 2016 and 2017, can result in enhanced thermal contrast by 3.3 °C. The function of the vegetation to lower the LST in a hot environment is evident. The results of this study suggest that the newly developed model provides an opportunity for urban planners and designers to develop measures for adjusting the LST, and for mitigating the consequent effects of UHIs by managing the land use composition and percentage coverage of the individual land-use type.
Nguyen Thanh Hoan; Yuei-An Liou; Kim-Anh Nguyen; Ram C. Sharma; Duy-Phien Tran; Chia-Ling Liou; Dao Dinh Cham. Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City. Remote Sensing 2018, 10, 1965 .
AMA StyleNguyen Thanh Hoan, Yuei-An Liou, Kim-Anh Nguyen, Ram C. Sharma, Duy-Phien Tran, Chia-Ling Liou, Dao Dinh Cham. Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City. Remote Sensing. 2018; 10 (12):1965.
Chicago/Turabian StyleNguyen Thanh Hoan; Yuei-An Liou; Kim-Anh Nguyen; Ram C. Sharma; Duy-Phien Tran; Chia-Ling Liou; Dao Dinh Cham. 2018. "Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City." Remote Sensing 10, no. 12: 1965.
This paper presents an assessment of the bidirectional reflectance features for the classification and characterization of vegetation physiognomic types at a national scale. The bidirectional reflectance data at multiple illumination and viewing geometries were generated by simulating the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters with Ross-Thick Li-Sparse-Reciprocal (RT-LSR) kernel weights. This research dealt with the classification and characterization of six vegetation physiognomic types—evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, and herbaceous—which are distributed all over the country. The supervised classification approach was used by employing four machine learning classifiers—k-Nearest Neighbors (KNN), Random Forests (RF), Support Vector Machines (SVM), and Multilayer Perceptron Neural Networks (NN)—with the support of ground truth data. The confusion matrix, overall accuracy, and kappa coefficient were calculated through a 10-fold cross-validation approach, and were also used as the metrics for quantitative evaluation. Among the classifiers tested, the accuracy metrics did not vary much with the classifiers; however, the Random Forests (RF; Overall accuracy = 0.76, Kappa coefficient = 0.72) and Support Vector Machines (SVM; Overall accuracy = 0.76, Kappa coefficient = 0.71) classifiers performed slightly better than other classifiers. The bidirectional reflectance spectra did not only vary with the vegetation physiognomic types, it also showed a pronounced difference between the backward and forward scattering directions. Thus, the bidirectional reflectance data provides additional features for improving the classification and characterization of vegetation physiognomic types at the broad scale.
Ram C. Sharma; Keitarou Hara. Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data. Geosciences 2018, 8, 394 .
AMA StyleRam C. Sharma, Keitarou Hara. Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data. Geosciences. 2018; 8 (11):394.
Chicago/Turabian StyleRam C. Sharma; Keitarou Hara. 2018. "Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data." Geosciences 8, no. 11: 394.
In recent years, the spatial resolution of satellite data has improved with advancements in satellite technology, and acquisition of even more detailed information on the surface of the earth can be expected in the future. Various complications, however, are still associated with retrieval and classification of ground surface information from very-high-resolution satellite data. One important issue is the occurrence of isolated pixels with high local spatial heterogeneity between neighbouring pixels in the classification result. The main objective of this research was to evaluate the effectiveness of two Multiple Classifier System (MCS), which combine the results from different supervised classifiers, as a means for reducing isolated pixels when using high-spatial resolution satellite imagery. The research was conducted in the Tohoku region, where the Great East Japan Earthquake of 11 March 2011 and subsequent tsunami have greatly changed the regional land use and land cover. Multiple input features (five RapidEye spectral channels, five spectral indices, digital elevation model, and slope) were prepared from satellite data and used for the machine learning and validation. Ground truth data belonging to six major land cover classes (forest, shrub/grassland, cropland, urban area, waterbody, bare ground) were collected. Six machine learning classifiers; Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbours (KNN), XGBoost, Bagging, and Neural Network (NNET), were individually tested for classification accuracy and generation of isolated pixels, and the results were compared with those of the MCS combinations. The XGBoost provided the highest overall accuracy (0.987) and kappa coefficient (0.985) of the individual classifiers, but suffered from a large number of isolated pixels. The MCS by combination weighing the kappa coefficients (MCS kappa), however, produced equivalent overall accuracy (0.989) and kappa coefficient (0.987), but at the same time was able to significantly reduce the number of isolated pixels. This reduction amounted to 30% for all land cover classes. In particular, the number of isolated pixels in the forest class was reduced by 50%. These results clearly show that the MCS kappa is capable of reducing isolated pixels in the classification of very-high-resolution land cover information, without sacrificing classification accuracy. The authors hope that this system will prove effective for monitoring fine-scale changes in land cover, and contribute to conserving landscapes and ecosystem services in the region impacted by the earthquake and tsunami.
Hidetake Hirayama; Ram C. Sharma; Mizuki Tomita; Keitarou Hara. Evaluating multiple classifier system for the reduction of salt-and-pepper noise in the classification of very-high-resolution satellite images. International Journal of Remote Sensing 2018, 40, 2542 -2557.
AMA StyleHidetake Hirayama, Ram C. Sharma, Mizuki Tomita, Keitarou Hara. Evaluating multiple classifier system for the reduction of salt-and-pepper noise in the classification of very-high-resolution satellite images. International Journal of Remote Sensing. 2018; 40 (7):2542-2557.
Chicago/Turabian StyleHidetake Hirayama; Ram C. Sharma; Mizuki Tomita; Keitarou Hara. 2018. "Evaluating multiple classifier system for the reduction of salt-and-pepper noise in the classification of very-high-resolution satellite images." International Journal of Remote Sensing 40, no. 7: 2542-2557.
Mapping the distribution of forested areas and monitoring their spatio-temporal changes are necessary for the conservation and management of forests. This paper presents two new image composites for the visualization and extraction of forest cover. By exploiting the Landsat-8 satellite-based multi-temporal and multi-spectral reflectance datasets, the Forest Cover Composite (FCC) was designed in this research. The FCC is an RGB (red, green, blue) color composite made up of short-wave infrared reflectance and green reflectance, specially selected from the day when the Normalized Difference Vegetation Index (NDVI) is at a maximum, as the red and blue bands, respectively. The annual mean NDVI values are used as the green band. The FCC is designed in such a way that the forested areas appear greener than other vegetation types, such as grasses and shrubs. On the other hand, the croplands and barren lands are usually seen as red and water/snow is seen as blue. However, forests may not necessarily be greener than other perennial vegetation. To cope with this problem, an Enhanced Forest Cover Composite (EFCC) was designed by combining the annual median backscattering intensity of the VH (vertical transmit, horizontal receive) polarization data from the Sentinel-1 satellite with the green term of the FCC to suppress the green component (mean NDVI values) of the FCC over the non-forested vegetative areas. The performances of the FCC and EFCC were evaluated for the discrimination and classification of forested areas all over Japan with the support of reference data. The FCC and EFCC provided promising results, and the high-resolution forest map newly produced in the research provided better accuracy than the extant MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type product (MCD12Q1) in Japan. The composite images proposed in the research are expected to improve forest monitoring activities in other regions as well.
Ram C. Sharma; Keitarou Hara; Ryutaro Tateishi. Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas. Journal of Imaging 2018, 4, 105 .
AMA StyleRam C. Sharma, Keitarou Hara, Ryutaro Tateishi. Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas. Journal of Imaging. 2018; 4 (9):105.
Chicago/Turabian StyleRam C. Sharma; Keitarou Hara; Ryutaro Tateishi. 2018. "Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas." Journal of Imaging 4, no. 9: 105.
This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets—with a sample size of 390—showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.
Ram C. Sharma; Keitarou Hara; Ryutaro Tateishi. High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach. Land 2017, 6, 50 .
AMA StyleRam C. Sharma, Keitarou Hara, Ryutaro Tateishi. High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach. Land. 2017; 6 (3):50.
Chicago/Turabian StyleRam C. Sharma; Keitarou Hara; Ryutaro Tateishi. 2017. "High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach." Land 6, no. 3: 50.
Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets asone of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset – GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes,which are urban, mangrove, wetland, snow/ice, and waterwere independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.
Toshiyuki Kobayashi; Ryutaro Tateishi; Bayan Alsaaideh; Ram C. Sharma; Takuma Wakaizumi; Daichi Miyamoto; Xiulian Bai; Bui D. Long; Gegentana Gegentana; Aikebaier Maitiniyazi; Destika Cahyana; Alifu Haireti; Yohei Morifuji; Gulijianati Abake; Rendy Pratama; Naijia Zhang; Zilaitigu Alifu; Tomohiro Shirahata; Lan Mi; Kotaro Iizuka; Aimaiti Yusupujiang; Fedri R. Rinawan; Richa Bhattarai; Dong X. Phong. Production of Global Land Cover Data – GLCNMO2013. Journal of Geography and Geology 2017, 9, 1 .
AMA StyleToshiyuki Kobayashi, Ryutaro Tateishi, Bayan Alsaaideh, Ram C. Sharma, Takuma Wakaizumi, Daichi Miyamoto, Xiulian Bai, Bui D. Long, Gegentana Gegentana, Aikebaier Maitiniyazi, Destika Cahyana, Alifu Haireti, Yohei Morifuji, Gulijianati Abake, Rendy Pratama, Naijia Zhang, Zilaitigu Alifu, Tomohiro Shirahata, Lan Mi, Kotaro Iizuka, Aimaiti Yusupujiang, Fedri R. Rinawan, Richa Bhattarai, Dong X. Phong. Production of Global Land Cover Data – GLCNMO2013. Journal of Geography and Geology. 2017; 9 (3):1.
Chicago/Turabian StyleToshiyuki Kobayashi; Ryutaro Tateishi; Bayan Alsaaideh; Ram C. Sharma; Takuma Wakaizumi; Daichi Miyamoto; Xiulian Bai; Bui D. Long; Gegentana Gegentana; Aikebaier Maitiniyazi; Destika Cahyana; Alifu Haireti; Yohei Morifuji; Gulijianati Abake; Rendy Pratama; Naijia Zhang; Zilaitigu Alifu; Tomohiro Shirahata; Lan Mi; Kotaro Iizuka; Aimaiti Yusupujiang; Fedri R. Rinawan; Richa Bhattarai; Dong X. Phong. 2017. "Production of Global Land Cover Data – GLCNMO2013." Journal of Geography and Geology 9, no. 3: 1.
Land use and land cover (LULC) change plays a key role in the process of land degradation and desertification in the Horqin Sandy Land, Inner Mongolia. This research presents a detailed and high-resolution (30 m) LULC change analysis over the past 16 years in Ongniud Banner, western part of the Horqin Sandy Land. The LULC classification was performed by combining multiple features calculated from the Landsat Archive products using the Support Vector Machine (SVM) based supervised classification approach. LULC maps with 17 secondary classes were produced for the year of 2000, 2009, and 2015 in the study area. The results showed that the multifeatures combination approach is crucial for improving the accuracy of the secondary-level LULC classification. The LULC change analyses over three different periods, 2000–2009, 2009–2015, and 2000–2015, identified significant changes as well as different trends of the secondary-level LULC in study area. Over the past 16 years, irrigated farming lands and salinized areas were expanded, whereas the waterbodies and sandy lands decreased. This implies increasing demand of water and indicates that the conservation of water resources is crucial for protecting the sensitive ecological zones in the Horqin Sandy Land.1. IntroductionDesertification is one of the crucial environmental issues that restrict the social, economic, and political development in arid and semiarid area [1, 2]. Desertification has been defined in several ways; however, the most widely accepted one has defined the desertification as the land degradation in arid, semiarid, and dry subhumid areas resulting from various factors, including climate variations and human activities [3–6]. In recent years, many socioenvironmental problems such as land shortage, environmental deterioration, reduction of biological and economic productivity, water scarcity, poverty, and migrations have emerged due to rapid spread of the desertification. These problems have threatened the human survival and sustainable economic development [7–9]. It is argued that sustainability will be a great challenge of the human society in coming decades particularly in the transitional and marginal agricultural zones [10–12].China is a developing country with large population and scarce arable land, which is plagued by a long-term and large-scale desertification [13]. In China, desertified areas are mostly distributed in the western part of the northeast China, the north part of the northern China, and most parts of the northwest China [14, 15]. The desert areas in China are still expanding by 2460–10,400 km2 per year. As much as 3.317 million km2 (34.6% of the total land area) land area in China is affected by the desertification; and up to 400 million people are struggling with unproductive agricultural land and water shortages [14]. The government of China and social media have focused on the desertification problems [15]. The government has implemented a series of ecological engineering programs to combat the desertification, including the Three-North Shelter Forest Program from 1978, Beijing and Tianjin Sandstorm Source Treatment Program from 2001 to 2010, Grain to Green Program from 2003, and Returning Grazing Land to Grassland Program from 2003 [13]. However, monitoring and assessment of these programs have identified very limited success in a few local regions [16–20], while desertification is increasing further in some desertification areas.The Horqin Sandy Land, one of the four largest sandy land in the northern China, has a long history of desertification and land degradation. Rapid increase in population and inappropriate human activities such as agricultural reclamation, overgrazing, excessive collection of fuel wood and herbs, unmanaged tourism, overconsumption of water resource, and mining and road cutting have induced the desertification continuously in the Horqin Sandy Land [18, 21, 22]. Moreover, climate change in the recent decades has severely intensified the desertification [23]. During Liao Dynasty (907–1125 AD), the Horqin Sandy Land was full of tall forests and dense grasslands which used to sustain nomadic herders [13]. Since the nineteenth century, rapid increase of agriculturalist migrants into this region started to convert the ancient grassland and woodland into agricultural areas which reduced the available grazing land and put the marginal lands under cultivation. This situation continued to twentieth century and reached the peak of development in the 1950s–1960s, with rapid expansion of the human settlements and urban areas [24]. In the beginning of the Great Leap Forward (1958–1960) and during the following two decades, expanding cultivation led to forcing the local nomadic herders to moving into the border area, and most of the area was occupied by agriculturalists [25, 26]. By the early 1980s, the rural reform program under the “household responsibility” system played a key role in the overgrazing of grassland; and now the implementation of “double responsibility” system by the local government may do little to reduce the overgrazing [24]. With the development in the agricultural and industrial sectors, these essentially uncontrolled activities have resulted in the destruction of woodland and grassland, degradation of surface soil, and increased water consumption [24].Accurate land use and land cover (LULC) maps derived from the remote sensing data are highly important for the monitoring and quantification of the global environment as well as spatiotemporal changes [27]. The LULC change analysis assists decision-makers to understand the dynamics of changing environment and can ensure the sustainable development [28]. Multitemporal and multiscale remote sensing can provide substantial information about the land surface and facilitate the monitoring of environmental problems such as land degradation [29]. The combination of spectral, textural, and topographic features has been suggested for improving the accuracy of LULC mapping while producing the recent nationwide 30 m resolution LULC map of Japan [30]. Since the previous studies in the Horqin Sandy Land have focused on mapping of the LULC and spatial-temporal change analysis mainly based on the spectral information from the satellite data [31], accuracy of the resulting change information is a major concern that is immensely important for the decision-makers.The availability of the high spatial resolution and multitemporal satellite imagery from the archived Landsat datasets provides a unique opportunity for the monitoring of land degradation and desertification. This study deploys the archived Landsat datasets of years 2000, 2009, and 2015 for deriving the high-resolution LULC change information to present detailed LULC change pattern. Since 2000, the state and local government implements a series of ecological restoration projects to mitigate the further desertification and restoration of desertified grassland in the fragile environment of the Horqin Sandy Land [13]. The main objective of this study is to analyze the spatiotemporal pattern of the LULC changes in the Horqin Sandy Land. This study presents the improvement of the LULC classification accuracy by combining the multiple features (spectral features, spectral indices, spectral transformations, and textural and topographic features) derived from the satellite data using the Support Vector Machine (SVM) based supervised classification approach. While the previous studies in the Horqin Sandy Land have analyzed the LULC changes using major land cover types only [11], this study has achieved the more detailed LULC change information by adding the secondary classes. The analysis on the spatiotemporal change pattern is expected to reveal the mechanisms responsible for the desertification processes.2. Materials and Methods2.1. Study AreaThis study was carried out in Ongniud Banner, in the western part of the Horqin Sandy Land in Inner Mongolia, China. The Ongniud Banner belongs to the transitional zone of the agricultural and animal husbandry region, which is vulnerable ecological region to natural changes and anthropogenic activities [32]. This region is severely suffering from soil erosion and overconsumption and overexploiting of land resources [32, 33]. The location map of the study area is shown in Figure 1. The study area covers an area of 11,882 km2, which stretches 250 km from east to west and 84 km from north to south.Figure 1: Location map of the study area (red polygon) displayed over the Landsat 8 data based false color composite (FCC) image.The three typical geomorphological characteristics throughout the study area are from west to east in the order of high elevation alluvial flats, low mountains and hills, and low Aeolian dunes, and the altitude decreases from 2025 m in the west to 286 m in the east [33]. The climate of this region is characterized by temperate semiarid with windy and dry winters/springs, warm and relatively rainy summers, and cool autumns. The mean annual temperature is 7°C; annual mean precipitation is 300 mm, of which 70% precipitation falls between July and September. The mean annual wind velocity is 4.2 m s−1 [34]; the windy season lasts from early March to late May. As the study area is comprised of the mosaic of farmland, grassland, and steppe desert with different soil types and land cover forms, this study area offers an opportunity to assess the performance of remote sensing data for change analysis of the detailed, secondary-level classes.2.2. Datasets and PreprocessingIn this research, the Landsat 5 Thematic Mapper (TM) datasets of years 2000 and 2009 and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) datasets of year 2015 available from the United States Geological Survey (USGS) were used. The details on the Landsat datasets used in the research are shown in Table 1. All the 30 m resolution Landsat scenes used in the research were carefully se
Xiulian Bai; Ram C. Sharma; Ryutaro Tateishi; Akihiko Kondoh; Bayaer Wuliangha; Gegen Tana. A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia. Mathematical Problems in Engineering 2017, 2017, 1 -13.
AMA StyleXiulian Bai, Ram C. Sharma, Ryutaro Tateishi, Akihiko Kondoh, Bayaer Wuliangha, Gegen Tana. A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia. Mathematical Problems in Engineering. 2017; 2017 ():1-13.
Chicago/Turabian StyleXiulian Bai; Ram C. Sharma; Ryutaro Tateishi; Akihiko Kondoh; Bayaer Wuliangha; Gegen Tana. 2017. "A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia." Mathematical Problems in Engineering 2017, no. : 1-13.
The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.
Ram C. Sharma; Ryutaro Tateishi; Keitarou Hara; Hoan Thanh Nguyen; Saeid Gharechelou; Luong Viet Nguyen. Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data. Sensors 2017, 17, 235 .
AMA StyleRam C. Sharma, Ryutaro Tateishi, Keitarou Hara, Hoan Thanh Nguyen, Saeid Gharechelou, Luong Viet Nguyen. Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data. Sensors. 2017; 17 (2):235.
Chicago/Turabian StyleRam C. Sharma; Ryutaro Tateishi; Keitarou Hara; Hoan Thanh Nguyen; Saeid Gharechelou; Luong Viet Nguyen. 2017. "Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data." Sensors 17, no. 2: 235.
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.
Ram C. Sharma; Keitarou Hara; Hidetake Hirayama; Ippei Harada; Daisuke Hasegawa; Mizuki Tomita; Jong Geol Park; Ichio Asanuma; Kevin M. Short; Masatoshi Hara; Yoshihiko Hirabuki; Michiro Fujihara; Ryutaro Tateishi. Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product. Advances in Remote Sensing 2017, 06, 54 -65.
AMA StyleRam C. Sharma, Keitarou Hara, Hidetake Hirayama, Ippei Harada, Daisuke Hasegawa, Mizuki Tomita, Jong Geol Park, Ichio Asanuma, Kevin M. Short, Masatoshi Hara, Yoshihiko Hirabuki, Michiro Fujihara, Ryutaro Tateishi. Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product. Advances in Remote Sensing. 2017; 06 (01):54-65.
Chicago/Turabian StyleRam C. Sharma; Keitarou Hara; Hidetake Hirayama; Ippei Harada; Daisuke Hasegawa; Mizuki Tomita; Jong Geol Park; Ichio Asanuma; Kevin M. Short; Masatoshi Hara; Yoshihiko Hirabuki; Michiro Fujihara; Ryutaro Tateishi. 2017. "Production of Multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product." Advances in Remote Sensing 06, no. 01: 54-65.
As the barren lands play a key role in the interaction between land cover dynamics and climate system, an efficient methodology for the global-scale extraction and mapping of the barren lands is important. The discriminative potential of the existing soil/bareness indexes was assessed by collecting globally distributed reference data belonging to major land cover types. The existing soil/bareness indexes parameterized at the local scale did not work satisfactorily everywhere at the global level. A new technique called the Biophysical Image Composite (BIC) is proposed in the research by exploiting time-series of the multi-spectral data to capture global-scale barren land attributes effectively. The BIC is a false color composite image made up of Normalized Difference Vegetation Index (NDVI), short wave infrared reflectance, and green reflectance, which were specially selected from the highest vegetation activity period by avoiding signals from the seasonal snowfall. The drastic contrast between the barren lands and vegetation as exhibited by the BIC provides a robust extraction and mapping of the barren lands, and facilitates its visual interpretation. Random Forests based supervised classification approach was applied on the BIC for the mapping of global barren lands. A new global barren land cover map of year 2013 was produced with high accuracy. The comparison of the resulted map with an existing map of the same year showed a substantial discrepancy between two maps due to methodological variation. To cope with this problem, the BIC based mapping methodology, with a special account of the land surface phenological changes, is suggested to standardize the global-scale estimates and mapping of the barren lands.
Ram C. Sharma; Ryutaro Tateishi; Keitarou Hara. A Biophysical Image Compositing Technique for the Global-Scale Extraction and Mapping of Barren Lands. ISPRS International Journal of Geo-Information 2016, 5, 225 .
AMA StyleRam C. Sharma, Ryutaro Tateishi, Keitarou Hara. A Biophysical Image Compositing Technique for the Global-Scale Extraction and Mapping of Barren Lands. ISPRS International Journal of Geo-Information. 2016; 5 (12):225.
Chicago/Turabian StyleRam C. Sharma; Ryutaro Tateishi; Keitarou Hara. 2016. "A Biophysical Image Compositing Technique for the Global-Scale Extraction and Mapping of Barren Lands." ISPRS International Journal of Geo-Information 5, no. 12: 225.
This research was carried out in a dense tropical forest region with the objective of improving the biomass estimates by a combination of ALOS-2 SAR, Landsat 8 optical, and field plots data. Using forest inventory based biomass data, the performance of different parameters from the two sensors was evaluated. The regression analysis with the biomass data showed that the backscatter from forest object (σ°forest) obtained from the SAR data was more sensitive to the biomass than HV polarization, SAR textures, and maximum NDVI parameters. However, the combination of the maximum NDVI from optical data, SAR textures from HV polarization, and σ°forest improved estimates of the biomass. The best model derived by the combination of multiple parameters from ALOS-2 SAR and Landsat 8 data was validated with inventory data. Then, the best validated model was used to produce an up-to-date biomass map for 2015 in Yok Don National Park, which is an important conservation area in Vietnam. The validation results showed that 74% of the variation of in biomass could be explained by our model.
Luong Viet Nguyen; Ryutaro Tateishi; Akihiko Kondoh; Ram C. Sharma; Hoan Thanh Nguyen; Tu Trong To; Dinh Ho Tong Minh. Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data. Land 2016, 5, 31 .
AMA StyleLuong Viet Nguyen, Ryutaro Tateishi, Akihiko Kondoh, Ram C. Sharma, Hoan Thanh Nguyen, Tu Trong To, Dinh Ho Tong Minh. Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data. Land. 2016; 5 (4):31.
Chicago/Turabian StyleLuong Viet Nguyen; Ryutaro Tateishi; Akihiko Kondoh; Ram C. Sharma; Hoan Thanh Nguyen; Tu Trong To; Dinh Ho Tong Minh. 2016. "Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data." Land 5, no. 4: 31.
An up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.
Ram C. Sharma; Ryutaro Tateishi; Keitarou Hara. A new water-resistant snow index for the detection and mapping of snow cover on a global scale. International Journal of Remote Sensing 2016, 37, 2706 -2723.
AMA StyleRam C. Sharma, Ryutaro Tateishi, Keitarou Hara. A new water-resistant snow index for the detection and mapping of snow cover on a global scale. International Journal of Remote Sensing. 2016; 37 (11):2706-2723.
Chicago/Turabian StyleRam C. Sharma; Ryutaro Tateishi; Keitarou Hara. 2016. "A new water-resistant snow index for the detection and mapping of snow cover on a global scale." International Journal of Remote Sensing 37, no. 11: 2706-2723.
Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.
Ram C. Sharma; Ryutaro Tateishi; Keitarou Hara; Kotaro Iizuka. Production of the Japan 30-m Land Cover Map of 2013–2015 Using a Random Forests-Based Feature Optimization Approach. Remote Sensing 2016, 8, 429 .
AMA StyleRam C. Sharma, Ryutaro Tateishi, Keitarou Hara, Kotaro Iizuka. Production of the Japan 30-m Land Cover Map of 2013–2015 Using a Random Forests-Based Feature Optimization Approach. Remote Sensing. 2016; 8 (5):429.
Chicago/Turabian StyleRam C. Sharma; Ryutaro Tateishi; Keitarou Hara; Kotaro Iizuka. 2016. "Production of the Japan 30-m Land Cover Map of 2013–2015 Using a Random Forests-Based Feature Optimization Approach." Remote Sensing 8, no. 5: 429.