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Territorial space classification (TSC) provides the basis for establishing systems of national territory spatial planning (NTSP) and supervising their implementation in China, thus has important theoretical and application significance. Most of the current TSC research is related to land use/land cover classification, ignoring the connection of the NTSP policies and systems, failing to consider the spatiotemporal heterogeneity of land use superior territorial space functions (TSFs) and the dynamic coupling between land use and its superior TSFs on the result of TSC. In this study, we integrated the factors influencing the connection of NTSP policies and systems and established a theoretical framework system of TSC from the perspective of spatial form and functional use. By integrating the
Dinghua Ou; Qi Zhang; Yijie Wu; Jing Qin; Jianguo Xia; Ouping Deng; Xuesong Gao; Jinhu Bian; Shangqi Gong. Construction of a Territorial Space Classification System Based on Spatiotemporal Heterogeneity of Land Use and Its Superior Territorial Space Functions and Their Dynamic Coupling: Case Study on Qionglai City of Sichuan Province, China. International Journal of Environmental Research and Public Health 2021, 18, 9052 .
AMA StyleDinghua Ou, Qi Zhang, Yijie Wu, Jing Qin, Jianguo Xia, Ouping Deng, Xuesong Gao, Jinhu Bian, Shangqi Gong. Construction of a Territorial Space Classification System Based on Spatiotemporal Heterogeneity of Land Use and Its Superior Territorial Space Functions and Their Dynamic Coupling: Case Study on Qionglai City of Sichuan Province, China. International Journal of Environmental Research and Public Health. 2021; 18 (17):9052.
Chicago/Turabian StyleDinghua Ou; Qi Zhang; Yijie Wu; Jing Qin; Jianguo Xia; Ouping Deng; Xuesong Gao; Jinhu Bian; Shangqi Gong. 2021. "Construction of a Territorial Space Classification System Based on Spatiotemporal Heterogeneity of Land Use and Its Superior Territorial Space Functions and Their Dynamic Coupling: Case Study on Qionglai City of Sichuan Province, China." International Journal of Environmental Research and Public Health 18, no. 17: 9052.
Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.
Jinhu Bian; Ainong Li; Xi Nan; Guangbin Lei; Zhengjian Zhang. Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019. Big Earth Data 2021, 1 -13.
AMA StyleJinhu Bian, Ainong Li, Xi Nan, Guangbin Lei, Zhengjian Zhang. Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019. Big Earth Data. 2021; ():1-13.
Chicago/Turabian StyleJinhu Bian; Ainong Li; Xi Nan; Guangbin Lei; Zhengjian Zhang. 2021. "Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019." Big Earth Data , no. : 1-13.
Large-scale projects, such as the construction of railways and highways, usually cause an extensive Land Use Land Cover Change (LULCC). The China-Central Asia-West Asia Economic Corridor (CCAWAEC), one key large-scale project of the Belt and Road Initiative (BRI), covers a region that is home to more than 1.6 billion people. Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor, reviewing LULCC mapping studies in this area has not been studied. This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area. The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor. To this end, 350 peer-reviewed journal and conference papers, as well as book chapters were analyzed based on 17 attributes, such as main driving factors of LULCC, data collection methods, classification algorithms, and accuracy assessment methods. It was observed that: (1) rapid urbanization, industrialization, population growth, and climate change have been recognized as major causes of LULCC in the study area; (2) LULCC has, directly and indirectly, caused several environmental issues, such as biodiversity loss, air pollution, water pollution, desertification, and land degradation; (3) there is a lack of well-annotated national land use data in the region; (4) there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area; and (5) several technical issues still require more attention from the scientific community. Finally, several recommendations were proposed to address the identified issues.
Amin Naboureh; Jinhu Bian; Guangbin Lei; Ainong Li. A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries. Big Earth Data 2020, 5, 237 -257.
AMA StyleAmin Naboureh, Jinhu Bian, Guangbin Lei, Ainong Li. A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries. Big Earth Data. 2020; 5 (2):237-257.
Chicago/Turabian StyleAmin Naboureh; Jinhu Bian; Guangbin Lei; Ainong Li. 2020. "A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries." Big Earth Data 5, no. 2: 237-257.
Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.
Amin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing 2020, 12, 3484 .
AMA StyleAmin Naboureh, Hamid Ebrahimy, Mohsen Azadbakht, Jinhu Bian, Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing. 2020; 12 (21):3484.
Chicago/Turabian StyleAmin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. 2020. "RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine." Remote Sensing 12, no. 21: 3484.
Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics.
Amin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing 2020, 12, 3301 .
AMA StyleAmin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing. 2020; 12 (20):3301.
Chicago/Turabian StyleAmin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. 2020. "A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions." Remote Sensing 12, no. 20: 3301.
Grazing intensity (GI) is an important indicator for grazing situations in pastoral areas. However, it has been difficult to be observed directly in the field, due to the randomness and dynamics of the grazing behavior of livestock. Consequently, the lack of actual GI information has become a common issue in studies on quantitatively estimating GI. In this paper, a novel quantitative estimation method is proposed based on the Space-Air-Ground integrated monitoring technology. It systematically integrates GPS tracking technology, Unmanned Aerial Vehicle (UAV) observation technology, and satellite remote sensing technology. Taking Xiangdong Village on the Zoige Plateau as a study area, the trajectory data and UAV images were acquired by the GPS tracking experiments and UAV observation experiments, respectively. The GI at paddock scale (PGI) was then generated with the Kernel Density Estimation (KDE) algorithm and the above data. Taking the generated PGI as training data, an estimation model of GI at region scale (RGI) was constructed by using the time-series satellite remote sensing images and random forest regression algorithm. Finally, the time-series RGI data with a spatial resolution of 10 m in Xiangdong Village were produced by the above model. The accuracy assessment demonstrated that the generated time-series RGI data could reflect the spatial-temporal heterogeneity of actual GI, with a mean absolute error of 0.9301 and r2 of 0. 8573. The proposed method provides a new idea for generating the actual GI on the ground and the time-series RGI data. This study also highlights the feasibility and potential of using the Space-Air-Ground integrated monitoring technology to generate time-series RGI data with high spatial resolution. The generated time-series RGI data would provide data support for the formulation of policies and plans related to the sustainable development of animal husbandry.
Guangbin Lei; Ainong Li; Zhengjian Zhang; Jinhu Bian; Guyue Hu; Changbo Wang; Xi Nan; Jiyan Wang; Jianbo Tan; Xiaohan Liao. The Quantitative Estimation of Grazing Intensity on the Zoige Plateau Based on the Space-Air-Ground Integrated Monitoring Technology. Remote Sensing 2020, 12, 1399 .
AMA StyleGuangbin Lei, Ainong Li, Zhengjian Zhang, Jinhu Bian, Guyue Hu, Changbo Wang, Xi Nan, Jiyan Wang, Jianbo Tan, Xiaohan Liao. The Quantitative Estimation of Grazing Intensity on the Zoige Plateau Based on the Space-Air-Ground Integrated Monitoring Technology. Remote Sensing. 2020; 12 (9):1399.
Chicago/Turabian StyleGuangbin Lei; Ainong Li; Zhengjian Zhang; Jinhu Bian; Guyue Hu; Changbo Wang; Xi Nan; Jiyan Wang; Jianbo Tan; Xiaohan Liao. 2020. "The Quantitative Estimation of Grazing Intensity on the Zoige Plateau Based on the Space-Air-Ground Integrated Monitoring Technology." Remote Sensing 12, no. 9: 1399.
Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%–8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.
Guangbin Lei; Ainong Li; Jinhu Bian; He Yan; Lulu Zhang; Zhengjian Zhang; Xi Nan. OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification. Remote Sensing 2020, 12, 987 .
AMA StyleGuangbin Lei, Ainong Li, Jinhu Bian, He Yan, Lulu Zhang, Zhengjian Zhang, Xi Nan. OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification. Remote Sensing. 2020; 12 (6):987.
Chicago/Turabian StyleGuangbin Lei; Ainong Li; Jinhu Bian; He Yan; Lulu Zhang; Zhengjian Zhang; Xi Nan. 2020. "OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification." Remote Sensing 12, no. 6: 987.
The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.
Jinhu Bian; Ainong Li; Jiaqi Zuo; Guangbin Lei; Zhengjian Zhang; Xi Nan; Bian; Li; Zuo; Lei; Nan. Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm. ISPRS International Journal of Geo-Information 2019, 8, 443 .
AMA StyleJinhu Bian, Ainong Li, Jiaqi Zuo, Guangbin Lei, Zhengjian Zhang, Xi Nan, Bian, Li, Zuo, Lei, Nan. Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm. ISPRS International Journal of Geo-Information. 2019; 8 (10):443.
Chicago/Turabian StyleJinhu Bian; Ainong Li; Jiaqi Zuo; Guangbin Lei; Zhengjian Zhang; Xi Nan; Bian; Li; Zuo; Lei; Nan. 2019. "Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm." ISPRS International Journal of Geo-Information 8, no. 10: 443.
The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area.
Abbas Naboureh; Bakhtiar Feizizadeh; Jinhu Bian; Thomas Blaschke; Omid Ghorbanzadeh; Meisam Moharrami. Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. ISPRS International Journal of Geo-Information 2019, 8, 371 .
AMA StyleAbbas Naboureh, Bakhtiar Feizizadeh, Jinhu Bian, Thomas Blaschke, Omid Ghorbanzadeh, Meisam Moharrami. Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. ISPRS International Journal of Geo-Information. 2019; 8 (9):371.
Chicago/Turabian StyleAbbas Naboureh; Bakhtiar Feizizadeh; Jinhu Bian; Thomas Blaschke; Omid Ghorbanzadeh; Meisam Moharrami. 2019. "Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services." ISPRS International Journal of Geo-Information 8, no. 9: 371.
Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.
Omid Ghorbanzadeh; Khalil Valizadeh Kamran; Thomas Blaschke; Jagannath Aryal; Amin Naboureh; Jamshid Einali; Jinhu Bian. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire 2019, 2, 43 .
AMA StyleOmid Ghorbanzadeh, Khalil Valizadeh Kamran, Thomas Blaschke, Jagannath Aryal, Amin Naboureh, Jamshid Einali, Jinhu Bian. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire. 2019; 2 (3):43.
Chicago/Turabian StyleOmid Ghorbanzadeh; Khalil Valizadeh Kamran; Thomas Blaschke; Jagannath Aryal; Amin Naboureh; Jamshid Einali; Jinhu Bian. 2019. "Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches." Fire 2, no. 3: 43.
The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.
Li He; Ainong Li; Gaofei Yin; Xi Nan; Jinhu Bian. Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery. Remote Sensing 2019, 11, 1597 .
AMA StyleLi He, Ainong Li, Gaofei Yin, Xi Nan, Jinhu Bian. Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery. Remote Sensing. 2019; 11 (13):1597.
Chicago/Turabian StyleLi He; Ainong Li; Gaofei Yin; Xi Nan; Jinhu Bian. 2019. "Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery." Remote Sensing 11, no. 13: 1597.
The South Asia has high variability in geographical features, climate, and landscapes. With the rapid economic development and population growth, the increased pressure on natural resources, land degradation, water crisis, and climate change become the common concerns for the countries in the region. To get a deep and general idea about the land and water natural resources and environment in South Asia, a knowledge database was constructed based on the climatic condition, land use/cover, water resources, water disasters, and geohazards. The results presented a scientific insight regarding the spatio-temporal changing pattern of the eco-environmental components in this region. Risk assessments were performed for the floods, droughts, and geohazards which occurred with a high frequency. In general, the big knowledge database established in this study would be helpful to aid the development of future policies and programs (like the Belt and Road Initiative) for environmental issues adaptation in the region, including initiatives for regional cooperation and capacity building in natural resources and environment management.
Ainong Li; Wei Deng; Wei Zhao; Bintao Liu; Jianqiang Zhang; Bo Kong; Xi Nan; Jinhu Bian; Hriday Lal Koirala; Hammad Gilani; Vishwambhar Prasad Sati; Pattiyage I. A. Gomes; Narendra Raj Khanal. A geo-spatial database about the eco-environment and its key issues in South Asia. Big Earth Data 2018, 2, 298 -319.
AMA StyleAinong Li, Wei Deng, Wei Zhao, Bintao Liu, Jianqiang Zhang, Bo Kong, Xi Nan, Jinhu Bian, Hriday Lal Koirala, Hammad Gilani, Vishwambhar Prasad Sati, Pattiyage I. A. Gomes, Narendra Raj Khanal. A geo-spatial database about the eco-environment and its key issues in South Asia. Big Earth Data. 2018; 2 (3):298-319.
Chicago/Turabian StyleAinong Li; Wei Deng; Wei Zhao; Bintao Liu; Jianqiang Zhang; Bo Kong; Xi Nan; Jinhu Bian; Hriday Lal Koirala; Hammad Gilani; Vishwambhar Prasad Sati; Pattiyage I. A. Gomes; Narendra Raj Khanal. 2018. "A geo-spatial database about the eco-environment and its key issues in South Asia." Big Earth Data 2, no. 3: 298-319.
The gross primary productivity (GPP) is an essential parameter of terrestrial carbon cycle, and simulation of GPP through terrestrial ecosystem process model usually needs a specification of model parameter. However, estimating model parameters in situ field or laboratory is a laborious and tedious work, causing a general lack of data. In this study, a reliable variational data assimilation scheme integrating Boreal Ecosystem Productivity Simulator (BEPS) with eddy covariance fluxes, was proposed to account for the seasonal variations of model parameters and improve temporally continuous GPP estimation. Results suggested that the proposed variational assimilation scheme in our study could effectively track the seasonal variations of model parameters. With optimal temporally continuous values of parameters, BEPS model had a better performance and potential ability for the GPP estimation.
Xinyao Xie; Ainong Li; Gaofei Yin; Jinhu Bian. Integrating Eddy Covariance Information with Beps Model Using a Variational Assimilation Scheme for Improving Temporally Continuous Gpp Estimation. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 9048 -9051.
AMA StyleXinyao Xie, Ainong Li, Gaofei Yin, Jinhu Bian. Integrating Eddy Covariance Information with Beps Model Using a Variational Assimilation Scheme for Improving Temporally Continuous Gpp Estimation. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():9048-9051.
Chicago/Turabian StyleXinyao Xie; Ainong Li; Gaofei Yin; Jinhu Bian. 2018. "Integrating Eddy Covariance Information with Beps Model Using a Variational Assimilation Scheme for Improving Temporally Continuous Gpp Estimation." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 9048-9051.
The implementation of the Belt and Road (B&R) initiative is of great significance to promoting economic development and regional cooperation in China and other countries along the corridors. Remote sensing technology can provide strong supports for ecological environment monitoring in the process of Belt and Road construction. However, due to frequent cloud contamination, the time series remote sensing images are not continuous in both spatial and temporal domain. This study proposed a new self-adaptive compositing approach (SACA) to produce the clear-sky VIIRS surface reflectance composites for the B&R regions. An evaluation was made for the SACA method by comparing it to the maximum NDVI (MaxNDVI), minimum Red (MinRed) and maximum ratio (MaxRatio) compositing schemes. Results showed that the SACA approach outperformed all other three methods. The results highlighted that the SACA method was feasible and effective at compositing continental VIIRS data, which has great potential for the B&R eco-environment studies.
Jinhu Bian; Ainong Li; Guangbin Lei. A New Self-Adaptive Approach for Producing Clear-Sky Viirs Composites at Continental Scale for the Studies of Belt and Road Initiative. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7255 -7258.
AMA StyleJinhu Bian, Ainong Li, Guangbin Lei. A New Self-Adaptive Approach for Producing Clear-Sky Viirs Composites at Continental Scale for the Studies of Belt and Road Initiative. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7255-7258.
Chicago/Turabian StyleJinhu Bian; Ainong Li; Guangbin Lei. 2018. "A New Self-Adaptive Approach for Producing Clear-Sky Viirs Composites at Continental Scale for the Studies of Belt and Road Initiative." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7255-7258.
As the flagship of the “Belt and Road”, the development and construction of the “China-Pakistan Economic Corridor (CPEC)” are related to the common interests of the country along the line. Gwadar port, which lies at the end of the China-Pakistan Economic Corridor, is extremely important for the CPEC project. The impervious surface percentage (ISP) is an important indicator of urban development assessment and urban ecological environment evaluation. In this study, the ISP of the Gwadar city is estimated by using the Chinese high spatial resolution GF-1/2 images and fine spatial resolution HJ-1A/B images from 2009 to 2017. The growth magnitude of the ISP in Gwadar is then analyzed. Results reveal that the estimated ISP are highly reliable for detecting and characterizing change trends. The R 2 (Correlation coefficient-squared) of imitating and predicting by using Random Forest method are 0.96 and 0.76 respectively. The root mean square error (RMSE) of the estimating results are approximately 12.52%. The results indicate that there are two stages in Gwadar's impervious surface growth. First stage is that there is a period of rapid growth from 2009 to 2014. The construction of residences and roads cause the fast increase at a rate of 2.3% per year. The other stage begins with the development of the China-Pakistan Economic Corridor. The greatest growth is in the port area from 2014 to 2017, and the growth magnitude is 0.06% per year.
Jiaqi Zuo; Jinhu Bian; Ainong Li; Guangbin Lei; Zegen Wang. Estimating Impervious Surfaces of Gwadar City Based on the Chinese Multi-Sources Remote Sensing Images. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 2631 -2634.
AMA StyleJiaqi Zuo, Jinhu Bian, Ainong Li, Guangbin Lei, Zegen Wang. Estimating Impervious Surfaces of Gwadar City Based on the Chinese Multi-Sources Remote Sensing Images. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():2631-2634.
Chicago/Turabian StyleJiaqi Zuo; Jinhu Bian; Ainong Li; Guangbin Lei; Zegen Wang. 2018. "Estimating Impervious Surfaces of Gwadar City Based on the Chinese Multi-Sources Remote Sensing Images." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 2631-2634.
The spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km × 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R2 = 0.64, RMSE = 48.13 g/m2) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB.
Gaofei Yin; Ainong Li; Chaoyang Wu; Jiyan Wang; Qiaoyun Xie; Zhengjian Zhang; Xi Nan; Huaan Jin; Jinhu Bian; Guangbin Lei. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance. ISPRS International Journal of Geo-Information 2018, 7, 242 .
AMA StyleGaofei Yin, Ainong Li, Chaoyang Wu, Jiyan Wang, Qiaoyun Xie, Zhengjian Zhang, Xi Nan, Huaan Jin, Jinhu Bian, Guangbin Lei. Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance. ISPRS International Journal of Geo-Information. 2018; 7 (7):242.
Chicago/Turabian StyleGaofei Yin; Ainong Li; Chaoyang Wu; Jiyan Wang; Qiaoyun Xie; Zhengjian Zhang; Xi Nan; Huaan Jin; Jinhu Bian; Guangbin Lei. 2018. "Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance." ISPRS International Journal of Geo-Information 7, no. 7: 242.
Due to the spatial heterogeneity of land surfaces, downscaling is an important issue in the development of carbon cycle models when evaluating the role of ecosystems in the global carbon cycle. In this study, a downscaling algorithm was developed to model gross primary productivity (GPP) at 500 m in a time series over rugged terrain, which considered the effects of spatial heterogeneity on carbon flux simulations. This work was carried out for a mountainous area with an altitude ranging from 2606 to 4744 m over the Gongga Mountain (Sichuan Province, China). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product at 1 km served as the primary dataset for the downscaling algorithm, and the 500 m MODIS GPP product was used as the reference dataset to evaluate the downscaled GPP results. Moreover, in order to illustrate the advantages and benefits of the proposed downscaling method, the downscaled results in this work, along with ordinary kriging downscaled results, spline downscaled results and inverse distance weighted (IDW) downscaled results, were compared to the MODIS GPP at 500 m. The results showed that (1) the GPP difference between the 500 m MODIS GPP and the proposed downscaled GPP results was primarily in the range of [−1, 1], showing that both vegetation heterogeneity factors (i.e., LAI) and topographic factors (i.e., altitude, slope and aspect) were useful for GPP downscaling; (2) the proposed downscaled results (R2 = 0.89, RMSE = 1.03) had a stronger consistency with the 500 m MODIS GPP than those of the ordinary kriging downscaled results (R2 = 0.43, RMSE = 1.36), the spline downscaled results (R2 = 0.40, RMSE = 1.50) and the IDW downscaled results (R2 = 0.42, RMSE = 1.10) for all Julian days; and (3) the inconsistency between MODIS GPP at 500 m and 1 km increased with the increase in altitude and slope. The proposed downscaling algorithm could provide a reference when considering the effects of spatial heterogeneity on carbon flux simulations and retrieving other fine resolution ecological-physiology parameters (e.g., net primary productivity and evaporation) over topographically complex terrains.
Xinyao Xie; Ainong Li; Huaan Jin; Gaofei Yin; Jinhu Bian. Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China. Remote Sensing 2018, 10, 647 .
AMA StyleXinyao Xie, Ainong Li, Huaan Jin, Gaofei Yin, Jinhu Bian. Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China. Remote Sensing. 2018; 10 (4):647.
Chicago/Turabian StyleXinyao Xie; Ainong Li; Huaan Jin; Gaofei Yin; Jinhu Bian. 2018. "Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China." Remote Sensing 10, no. 4: 647.
Jianbo Tan; Ainong Li; Guangbin Lei; Jinhu Bian; Guoke Chen; Keping Ma. Preliminary assessment of ecosystem risk based on IUCN criteria in a hierarchy of spatial domains: A case study in Southwestern China. Biological Conservation 2017, 215, 152 -161.
AMA StyleJianbo Tan, Ainong Li, Guangbin Lei, Jinhu Bian, Guoke Chen, Keping Ma. Preliminary assessment of ecosystem risk based on IUCN criteria in a hierarchy of spatial domains: A case study in Southwestern China. Biological Conservation. 2017; 215 ():152-161.
Chicago/Turabian StyleJianbo Tan; Ainong Li; Guangbin Lei; Jinhu Bian; Guoke Chen; Keping Ma. 2017. "Preliminary assessment of ecosystem risk based on IUCN criteria in a hierarchy of spatial domains: A case study in Southwestern China." Biological Conservation 215, no. : 152-161.
Nepal, located in a unique transition zone spanning from plains to mountains and then to the plateau, is characterized by diverse and complex land cover. Based on an object-oriented method and decision tree classifier, a land cover product covering the whole of Nepal in 2010 (hereinafter referred to as the NepalCover-2010) was produced using 30 m-resolution Landsat TM images, consisting of 8 classes at Level I and 31 classes at Level II. The accuracy of the NepalCover-2010 product at Level II was validated using samples collected from high-resolution Google Earth images. The result showed that the overall accuracy of the product was 87.17%, with a Kappa coefficient of 0.85, making it the most accurate product among similar land cover products. The product can accurately reflect the spatial patterns of land cover in Nepal. Forests are the main land cover classes, accounting for 41% of the land, followed by croplands covering about 25%. The areal proportion of paddy fields to dry farmlands was approximately two to three. Topographical and meteorological factors presented as the determining effects on the spatial patterns of land cover in Nepal. With elevation uplift from south to north, land cover classes showed a vertical zonality ordered thus: paddy fields, evergreen broadleaf forests, dry farmlands, evergreen broadleaf shrubs, evergreen needleleaf forests, grasslands, sparse vegetation, and permanent ice/snow. Land cover mapping in Nepal contributes significantly to the basic data collection in this country, and can also be of a benefit to China’s international regional economic cooperation strategy entitled “the Belt and Road Initiative”.
Guangbin Lei; Ainong Li; Xiaomin Cao; Wei Zhao; Jinhu Bian; Wei Deng; Hriday Lal Koirala. Land Cover Mapping and Its Spatial Pattern Analysis in Nepal. Governance in Transition 2017, 17 -39.
AMA StyleGuangbin Lei, Ainong Li, Xiaomin Cao, Wei Zhao, Jinhu Bian, Wei Deng, Hriday Lal Koirala. Land Cover Mapping and Its Spatial Pattern Analysis in Nepal. Governance in Transition. 2017; ():17-39.
Chicago/Turabian StyleGuangbin Lei; Ainong Li; Xiaomin Cao; Wei Zhao; Jinhu Bian; Wei Deng; Hriday Lal Koirala. 2017. "Land Cover Mapping and Its Spatial Pattern Analysis in Nepal." Governance in Transition , no. : 17-39.
The devastating earthquake that occurred in Nepal on April 25, 2015, caused widespread damage and ravaged rural communities and economy. It triggered numerous geohazards in the steep mountains and hills throughout the impact zone, including catastrophic landslides in the Langtang Valley. Potentially, these landslides can have devastating effects on humans, destroy infrastructure, obstruct river flows and cause outburst floods. In order to understand the effects of the earthquake-induced geohazards and their risk assessment, number of scientists have carried out numerous geohazard investigations using remote sensing approach. This paper presents evaluation results based on the study of earthquake-related geohazards. Later, three main geohazards related to the Langtang avalanche, landslide dam and glacial lake monitoring are discussed in detail. Finally, the zones around the two China–Nepal roads are selected as the study areas. A change detection method is applied to identify the geohazards occurred in these areas and to understand their spatial distribution pattern. The study offers awareness about the earthquake-induced geohazards in Nepal. Landslide is identified as the major geohazard induced by earthquake, and its regional impact will continue in near future.
Wei Zhao; Ainong Li; Zhengjian Zhang; Guangbin Lei; Jinhu Bian; Wei Deng; Narendra Raj Khanal. Investigation and Analysis of Geohazards Induced by the 2015 Nepal Earthquake Based on Remote Sensing Method. Governance in Transition 2017, 427 -444.
AMA StyleWei Zhao, Ainong Li, Zhengjian Zhang, Guangbin Lei, Jinhu Bian, Wei Deng, Narendra Raj Khanal. Investigation and Analysis of Geohazards Induced by the 2015 Nepal Earthquake Based on Remote Sensing Method. Governance in Transition. 2017; ():427-444.
Chicago/Turabian StyleWei Zhao; Ainong Li; Zhengjian Zhang; Guangbin Lei; Jinhu Bian; Wei Deng; Narendra Raj Khanal. 2017. "Investigation and Analysis of Geohazards Induced by the 2015 Nepal Earthquake Based on Remote Sensing Method." Governance in Transition , no. : 427-444.