关键词:
Ice albedo;data harmonization;spatial window size;validation;arctic and alpine;Google Earth Engine
摘要:
Albedo plays a key role in regulating the absorption of solar radiation within ice surfaces and hence strongly regulates the production of meltwater. A combination of Landsat and Sentinel 2 data provides the longest continuous medium resolution (10-30 m) earth surface observatory records. An albedo product (harmonized satellite albedo, hereafter HSA) has already been developed and validated for the Greenland Ice Sheet (GrIS), using harmonized Landsat 4-8 and Sentinel 2 datasets. In this paper, the HSA was validated for various Arctic and alpine glaciers and ice caps using in situ measurements. We determine the optimal spatial window size in point-to-pixel analysis, the best practices in evaluating remote sensing algorithms with groundtruth data, and cross sensor comparison of the Landsat 9 (L9) and Landsat 8 (L8) data. The impact of the spatial window size on measured ice surface homogeneity and albedo validation was analysed at both local and regional scales. Homogeneity statistics calculated from the grey-level co-occurrence matrix (GLCM) suggest that the ice surface becomes more homogeneous as the image resolution becomes coarser. The optimal spatial window size was found to be 90 m, based on maximizing the statistical and graphical measures while minimizing the root mean square error and bias. HSAs generally agree closely with in situ albedo measurements (e.g. Pearson's R ranges from 0.68 to 0.92) across various Arctic and alpine glaciers and ice caps. Cross sensor differences between L9 and L8 are minor, and we suggest that no harmonization is necessary to add L9 to our HSA product.
作者机构:
[Han, Yong; Ni, Ruixing; Deng, Yating] Xinyang Normal Univ, Sch Geog Sci, Xinyang 464000, Peoples R China.;[Zhu, Yuanyuan; Zhu, YY] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
通讯机构:
[Zhu, YY ] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
摘要:
The imbalanced regional development of higher vocational education, particularly the disparity between the supply and demand of educational resources, has emerged as the primary factor impeding the provision of high-quality higher education in China during the establishment of a universal education system. Based on the 1,482 higher vocational education institutions recognized by the Ministry of Education of China in 2021 as the research objects, the development of higher vocational education in China was explored from the perspective of supply and demand using the entropy weight TOPSIS method and coupling coordination degree model. It was found that China's higher vocational institutions were mainly located in provincial capitals, representing a point distribution pattern. From a comprehensive evaluation of the supply level, areas such as the Beijing-Tianjin-Hebei region, Yangtze River Delta, and central Henan Province have become the catchment areas for the development of higher vocational education, laying the foundation for regional network cooperation. From the perspective of educational equality, the higher vocational education in China was found to be sufficient to match the supply and demand, and a balance between supply and demand was apparent in provincial capitals. The coupling degree between supply and demand exhibited an "olive-type" spatial structure pattern, indicating that the development of higher vocational education in most cities in China is still in the transformation stage. The results provide a scientific basis for optimizing resources in the provision of higher vocational education.
作者机构:
[Fu, Yongshuo H.; Xiao, Yi] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China.;[Hao, Fanghua; Guo, Yahui; Chen, Jiahao; Nie, Xingyu; Li, Xiran; Xu, Yue; Guo, Shihui] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Fu, YH ] B;Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China.
关键词:
High spatial resolution;Optical Satellite;Inland waters;Chlorophyll-a;Suspended sediment;Unmanned aerial vehicle (UAV)
摘要:
In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change and human activities. Given the generally narrow width of rivers and current high spatial resolution satellites, which are limited by band settings, bandwidth, and the signal-to-noise ratio, UAVs with their exceptional spatiotemporal resolution can be used as a useful tool for river environmental monitoring and inversion uncertainty assessment. In this study, UAV-based hyperspectral (X20P) and multispectral (P4M) images, along with Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 data, were used to assess the uncertainty in retrieving chlorophyll-a (Chla) and suspended sediment (SS) concentrations in rivers. Chla and SS models based on UAV and satellite data were constructed using stepwise multiple regression and typical Chla and SS retrieval algorithms, respectively, and the performance of the models was the focus of our research. The results demonstrated that in the Chla concentration inversion, each sensor performed as follows: X20P > P4M > Landsat9 OLI2 > Sentinel-2 MSI > Landsat8 OLI, and the performance in the SS concentration inversion was as follows: X20P > Sentinel-2 MSI > P4M > Landsat9 OLI2 > Landsat8 OLI. In addition, the uncertainty of high spatial resolution satellite retrievals was analyzed with the assistance of the UAV-based model. Results showed that narrow bandwidths and finely tuned band settings are more essential for the Chla inversion. The typical Chla retrieval algorithm, NDCI, is only effective in certain bands (band 1 from 684 to 724 nm and band 2 from 660 to 680 nm). It is also noted that Landsat8 and Landsat9 lack some key band settings (e.g., the red-edge band of 700-710 nm), severely limiting practical application in relation to Chla. However, specific variances in different sensor bands have a relatively small impact on SS inversion, for example, the correlation between SS and the R/B (a typical SS retrieval algorithm) constructed by each sensor ranged from 0.68 to 0.77. Chla monitoring, on the other hand, necessitates a higher spatial resolution than SS monitoring. The accuracy decreased markedly when UAV images were resampled to 10 m and 30 m spatial resolution. However, it is not as crucial for the SS inversion, images with the original spatial resolution (RMSE<30cm = 6.28 mg/L) were resampled to 10 m resolution (RMSE10m = 5.85 mg/L) and 30 m resolution (RMSE30m = 4.08 mg/L) while using P4M for SS inversion, and the accuracy increased. Our results demonstrated and highlighted various options for future monitoring of Chla and SS, while exploiting the synergy between UAVs and satellites to achieve more precise observations at greater spatial and temporal scales, which will benefit aquatic environment management and protection.
作者机构:
[Zheng, Wensheng; Xiong, Yajun; Wang, Xuzheng; Wang, Xiaofang; Zhou, Ying] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zheng, Wensheng; Wang, Xiaofang] China Tourism Acad, Wuhan Branch, Wuhan 430079, Peoples R China.;[Zheng, Wensheng] Cent China Normal Univ, Hubei High qual Dev Inst, Wuhan 430079, Peoples R China.
通讯机构:
[Wensheng Zheng] C;College of Urban and Environment Science, Central China Normal University, Wuhan, China<&wdkj&>Wuhan Branch of China Tourism Academy, Wuhan, China<&wdkj&>Hubei High-quality Development Institute, Central China Normal University, Wuhan, China
作者机构:
[Xu, Baodong; Wei, Haodong; Xu, Zilu; Yang, Jingya; Cai, Zhiwen] Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Hu, Qiong; He, Zhen] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[You, Liangzhi] Huazhong Agr Univ, Coll Econ & Management, Wuhan 430070, Peoples R China.;[You, Liangzhi] Int Food Policy Res Inst, 1201 1 St NW, Washington, DC 20005 USA.;[Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China.
通讯机构:
[Baodong Xu] M;Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China<&wdkj&>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
关键词:
Ratoon rice;Potential northern limits;Potential planting areas;Climate conditions;MaxEnt model
摘要:
Ratoon rice has emerged as a promising rice cropping system to improve grain production and reduce labor costs compared with traditional single/double rice in China. However, the potential planting areas of ratoon rice in China remain unclear. This research investigated the potential northern limits and promotion extent of ratoon rice in China by considering its climatic suitability based on the optimized maximum entropy (MaxEnt) model as well as terrain and land use conditions. The MaxEnt model derived by all environmental variables yielded a good performance, with average AUC (area under the curve) and TSS (true skill statistic) over the validation dataset of 0.940 and 0.825, respectively. The comparison with field samples and previous studies revealed the reliability of the derived potential promotion areas. Potential northern limits contained a closed curve surrounding the Sichuan Basin, and the other curve ran from Yunnan Province to Jiangsu Province. Safe promotion areas of ratoon rice in China were 472,003 km2, mainly located in Sichuan, Hubei, Guangxi and Hunan. Risky promotion areas were 74,150 km2, which were dominant in Henan, Anhui and Yunnan. Our study provides crucial infor-mation for rice planting pattern adjustment to alleviate national food insecurity caused by the loss of double rice.
通讯机构:
[Yonglong Lu] S;State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian 361102, China<&wdkj&>State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
关键词:
Energy Modeling;Energy management;Energy policy;Energy resources
摘要:
The flourishing logistics in both developed and emerging economies leads to huge greenhouse gas (GHG) emissions; however, the emission fluxes are poorly constrained. Here, we constructed a spatial network of logistic GHG emissions based on multisource big data at continental scale. GHG emissions related to logistics transportation reached 112.14Mt CO(2)-equivalents (CO(2)e), with seven major urban agglomerations contributing 63% of the total emissions. Regions with short transport distances and well-developed road infrastructure had relatively high emission efficiency. Underlying value flow of the commodities is accompanied by logistics carbon flow along the supply chain. The main driving factors affecting GHG emissions are driving speed and gross domestic product. It may mitigate GHG emissions by 27.50-1162.75 Mt CO(2)e in 15 years if a variety of energy combinations or the appropriate driving speed (65-70km/h) is adopted. The estimations are of great significance to make integrated management policies for the global logistics sector.
期刊:
ISPRS Journal of Photogrammetry and Remote Sensing,2023年204:397-420 ISSN:0924-2716
通讯作者:
Meng, R;Zhao, F
作者机构:
[Meng, Ran; Lv, Zhengang; Zhou, Longfei; Meng, R; Huang, Zehua; Xu, Binyuan; Sun, Rui] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Zhong, Liheng] Ant Grp, Hangzhou 311121, Peoples R China.;[Zhao, Feng] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Wu, Jin] Univ Hong Kong, Sch Biol Sci, Hong Kong, Peoples R China.;[Wu, Jin] Chinese Univ Hong Kong, State Key Lab Agrobiotechnol, Shatin, Hong Kong, Peoples R China.
通讯机构:
[Zhao, F ] C;[Meng, R ] H;Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;HIT Artificial Intelligence Res Inst Co Ltd, Harbin, Heilongjiang, Peoples R China.
关键词:
Tree species mapping;Key phenological stage;Transformer;Attention mechanism;Deep learning;Plantation forests
摘要:
Plantation forests provide critical ecosystem services and have experienced worldwide expansion during the past few decades. Accurate mapping of tree species through remote sensing is critical for managing plantation forests. The typical temporal behaviors and traits of tree species in satellite image time series (SITS) generate temporal and spectral features in multiple phenological stages that are critical to improve tree species mapping. However, the diverse input features, sequential relations and complex structures in SITS drastically increase the dimension and difficulty of spectral-temporal feature extraction, which challenges the capacity of many general classifiers not explicitly adapted for spectral-temporal learning. As a result, there is still a lack of a method that could automatically extract spectral-temporal features with high separability and regional adaptability from highdimensional SITS for tree species mapping of plantation forests. Moreover, the effects of varying temporal resolution and feature combination on the plantation tree species mapping are under-explored. Here, we developed a multi-head attention-based method for automatically extracting spectral-temporal features with high separability based on a modified Transformer network (Transformer4SITS) for improved plantation tree species mapping. The end-to-end network model consists of a feature extraction module to learn deep spectral-temporal features from SITS and a fusion module to combine multiple features for improving mapping accuracy. We applied this method to two representative plantation forests in southern and northern China for tree species mapping. The results show that: (1) Transformer4SITS method could self-adaptively extract typical spectraltemporal features of key phenological stages (e.g., greenness rising and falling) from SITS, and achieved significantly improved accuracies by at most 15% in comparison with all four baseline methods (i.e., long shortterm memory, harmonic analysis, time-weighted dynamic time warping, linear discriminant analysis); (2) time series with higher temporal resolution tended to produce more accurate species maps consistently across two sites, with their overall accuracies (OA) respectively increasing from 91.05% and 84.33% (60-day) to 94.88% and 88.72% (5-day), but the effect of high temporal resolution respectively leveled off around 90-day and 50-day resolution across two sites; (3) the mapping results using all available bands and two-band spectral indices outperformed the results using a subset of them, but with only modest increase in the accuracy (i.e., OA increased from 93.63% and 86.01% to 94.88% and 88.72%. This study thus provides a state-of-the-art deep learning-based method for improved tree species mapping, which is critical for sustainable management and biodiversity monitoring of plantation forests across large scales.
摘要:
Abstract: A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People’s Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods. Keywords: remote sensing dataset; deep learning; soil water erosion assessment; object-oriented image classification; land-use/land-cover mapping
作者机构:
[Zhu, Wenchao; Wu, Hao; Jiang, Zhimeng; Cen, Luyu] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zhu, Wenchao; Wu, Hao; Jiang, Zhimeng; Cen, Luyu] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.
通讯机构:
[Zhimeng Jiang] C;College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China<&wdkj&>Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, China
关键词:
land use spatial pattern;resource environment carrying capacity;land use change;spatial optimization;high-quality land development;Zhengzhou city
摘要:
High-intensity land use and resource overloaded-induced regional land use spatial pattern (LUSP) are essential and challenging for high-quality development. The empirical studies have shown that a scientific land uses spatial layout, and the supporting system should be based on a historical perspective and require better considering the double influence between the current characteristics and future dynamics. This study proposes a comprehensive framework that integrates the resource environment carrying capacity (RECC) and land use change (LUC) to investigate strategies for optimizing the spatial pattern of land use for high-quality development. China's Zhengzhou city was the subject of a case study whose datasets include remote sensing, spatial monitoring, statistics, and open sources. Three significant results emerged from the analysis: (1) The RECC has significant spatial differentiation but does not follow a specific spatial law, and regions with relatively perfect ecosystems may not necessarily have better RECC. (2) From 2020 to 2030, the construction land and farmland will fluctuate wildly, with the former increasing by 346.21 km(2) and the latter decreasing by 295.98 km(2). (3) The study area is divided into five zones, including resource conservation, ecological carrying, living core, suitable construction, and grain supply zones, and each one has its LUSP optimization orientation. This uneven distribution of RECC reflects functional defects in the development and utilization of LUSP. In addition, the increase in construction land and the sharp decline of farmland pose potential threats to the sustainable development of the study area. Hence, these two elements cannot be ignored in the future high-quality development process. The findings indicate that the LUSP optimization based on dual dimensions of RECC and LUC is more realistic than a single-dimension solution, exhibiting the LUSP optimization's effectiveness and applicability.
期刊:
European Journal of Agronomy,2023年151 ISSN:1161-0301
通讯作者:
Xu, BD;Hu, Q
作者机构:
[Xu, Baodong; Xu, BD; Zhou, Wei; Cai, Zhiwen] Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Hu, Jie; Zhang, Xinyu; Wei, Haodong; Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China.;[Yang, Jingya] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China.;[Hu, Q; Hu, Qiong] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Xiong, Hang] Huazhong Agr Univ, Coll Econ & Management, Wuhan 430070, Peoples R China.
通讯机构:
[Hu, Q ] C;[Xu, BD ] H;Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
关键词:
Plastic -mulched citrus index;Spectral separability;Intra-annual and interannual analysis;Spatio-temporal variation;Multi -source remote sensing data
摘要:
The technology of canopy plastic mulching has been widely used in citrus orchards for protecting fruit trees from cold damage. Understanding the spatio-temporal dynamics of plastic-mulched citrus (PMC) is of great importance for precision management of citrus orchards. However, monitoring the long-term and large-area PMC dynamics is challenging because the PMC is typically distributed in cloudy and rainy areas with rapid spatial variability, leading to the limited availability of high-quality remotely sensed data. Moreover, it is difficult to collect sufficient field samples in rugged mountainous regions for extracting PMC. To address these limitations, we proposed a new plastic-mulched citrus index (PMCI) based on spectral separability analysis of PMC and other land cover types. The images from Landsat-5/7/8, Sentinel-2, and Gaofen-1 satellites were employed to extract the intra-annual PMC distribution from 2019 to 2020 and interannual PMC distribution from 2008 to 2020 in Yangshuo county, Guangxi Zhuang Autonomous Region, China. Results showed that the PMCI outperformed other widely used indices in PMC extraction with overall accuracy (OA) increased by 0.09-0.3. Besides, the PMCI exhibited good performances in extracting PMC over different observation dates with OA ranging 0.92-0.98 and 0.91-0.98 in the intra-annual and annual PMC maps, respectively. According to the derived PMC time series maps, PMC displayed significant intra-annual spatial variations in the start date and length of plastic mulching period, whereas the interannual variation of PMC revealed long-term technology adoption patterns in the study area. Furthermore, we found that temperature mainly affected intra-annual PMC variability, and that agricultural policy, market factor, neighborhood effect and variety replacement can explain the interannual PMC variability. These results indicate that the developed PMCI can effectively identify PMC over different agricultural regions and observation dates, and the resultant spatio-temporal dynamic PMC maps based on multi-source satellite data provide an important basis for the promotion of PMC at larger scales and the sustainable development of the citrus industry.