作者机构：
[Liu, Na; Huang, Yimin; Huang, Linjuan; Jiang, Wulin] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;[Jiang, Wulin] Hengyang Normal Univ, Cooperat Innovat Ctr Digitalizat Cultural Heritag, Hengyang 421002, Peoples R China.;[Jiang, Wulin] Int Ctr Space Technol Nat & Cultural Heritage HIS, Hengyang Base 421002, Hengyang, Peoples R China.;[Li, Yilong] Hunan Climate Ctr, Changsha 410118, Peoples R China.;[Xiao, Xiong; Zhang, Cicheng] Hunan Normal Univ, Sch Geog Sci, Changsha 410081, Peoples R China.
通讯机构：
[Na Liu] C;College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
water resources;climatic factors;human activities;Hunan Province
摘要：
The demand for social and economic development has promoted research into water resources. The combined effects of natural conditions and human activities on regional water resource usage are not well understood. The sustainable utilization of water resources was assessed in terms of supply (e.g., precipitation) and demand (e.g., ecological water resources footprint (EFw)) sides in Hunan Province, China, from 2010 to 2019. The results showed that: (1) on the supply side, water resources were increased across Hunan Province. The spatial patterns of total water resources are significantly heterogeneous, with high values in the east and south, which are mainly affected by precipitation; (2) on the demand side, evapotranspiration was great in areas with high vegetation coverage. The EFw was high in relatively developed areas. The mean percentage of agricultural EFw remained dominant at approximately 60% with a steady decreasing trend, while that of eco-environmental EFw increased; and (3) the sustainable utilization of water resources in Hunan Province is generally rational. Moreover, the potential for water resource development and utilization is really significant in eastern and southern Hunan Province. The findings are beneficial in providing an important scientific basis for policymaking relating to the efficient utilization of regional water resources.
作者机构：
[Zhang, Fanyi; Tian, Xin] Southeast Univ, Sch Transportat, Dept Surveying & Mapping Engn, Nanjing 211189, Peoples R China.;[Zhang, Haibo] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;[Jiang, Mi] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 510275, Peoples R China.
通讯机构：
[Xin Tian] D;Department of Surveying and Mapping Engineering, School of Transportation, Southeast University, Nanjing 211189, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.
摘要：
Abstract: Shanggantang Village in Jiangyong County is an ancient village with a history of a thousand-years-old village, which is very valuable for tourism development and utilization. To sum up its village characteristics, first, the clan culture has a long vein, second, the cultural landscape is unique, and third, the emerging cultural celebrities are prominent. However, its tourism development and utilization are faced with two outstanding problems, one is the fragile ecology, and the other is the prominent contradiction between humans and land. Therefore, in the process of its development and utilization, we must pay attention to and strive to solve four problems, first, adhere to the harmony between humans and nature; second, vigorously promote green development; third solve outstanding environmental problems; fourth, increase the intensity of ecological environment protection. For this reason, we must adhere to the human beauty of “government leading, villagers participating, and creating together”, and “the whole village lives in the picture without people’s awareness”, so as to make Shanggantang village better.#@#@#摘要: 江永县的上甘棠村是千年古村落，极富旅游开发利用价值。归纳其村落特色，一是氏族文化脉络绵长，二是文化景观绝胜，三是所涌现出来的文化名人地位显赫。但其旅游开发利用面临两个突出问题，一是生态脆弱，二是人地矛盾突出。因此，在其开发利用过程中必须重视并力求解决四个方面的问题，一是坚持人与自然和谐，二是大力推进绿色发展，三是解决环境中存在的突出问题，四是加大生态环境保护力度。为此，我们必须坚持“政府主导，村民参与”，共同营造“山深人不觉，全村同在画中居”的人间美景，使上甘棠村建设得更加美好。
摘要：
Given environmental or hydrological functions influenced by changing river networks in the development of rapid urbanization, a clear understanding of the relationships between comprehensive urbanization (CUB) and river network characteristics (RNC), storage capacity (RSC), and regulation capacity (RRC) is urgently needed. In the rapidly urbanized Tai Lake Plain (TLP), China, various methods and multisource data were integrated to estimate the dynamics of RNC, RSC, and RRC as well as their interactions with urbanization. The bivariate Moran's I methods were applied to detect and visualize the spatial dependency of RNC, RSC, or RRC on urbanization. Geographically weighted regression (GWR) model was set up to characterize spatial heterogeneity of urbanization influences on RNC, RSC and RRC. Our results indicated that RNC, RSC and RRC variables each showed an overall decreasing trend across space from 1960s to 2010s, particularly in those of tributary rivers. RNC, RSC, or RRC had globally negative correlations with CUB, respectively, but looking at local scale the spatial correlations between each pair were categorized as four types: high-high, high-low, low-low, and low-high. GWR was identified to accurately predict the response of most RNC, RSC, or RRC variables to CUB (R-2: 0.6-0.8). The predictive ability of GWR was spatially non-stationary. The obtained relationships presented different directions and strength in space. All variables except for the water surface ratio (Wp) were more positively affected by CUB in the middle eastern parts of TLP. Drainage density, RSC and RRC variables were more negatively influenced by CUB in the northeast compared to other parts. The quantitative results of spatial relationships between urbanization and RNC, RSC or RRC can provide location-specific guidance for river environment protection and regional flood risk management.
作者机构：
[Liu, Peilin; Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;[Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Cooperat Innovat Ctr Digitalizat Cultural Heritag, Hengyang 421002, Peoples R China.;[Han, Qing; Deng, Yunyuan] Hengyang Normal Univ, Natl Local Joint Engn Lab Digital Preservat & Inn, Hengyang 421002, Peoples R China.;[Yin, Chao] Guangdong Acad Sci, Guangzhou Inst Geog,Guangdong Open Lab Geospatial, Guangdong Prov Engn Lab Geog Spatiotemporal Big D, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Peoples R China.;[Yin, Chao] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong 999077, Peoples R China.
通讯机构：
[Chao Yin] D;Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China<&wdkj&>Guangdong Province Engineering Laboratory for Geographic Spatio-temporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep learning (DL), some recent studies attempted to apply DL networks such as convolution neural networks (CNNs) to achieve automated classification of the architecture styles. However, these studies suffer overfitting problems of the CNNs, leading to inferior classification performance. Moreover, most of the studies apply the CNNs as a black box providing limited interpretability. To address these limitations, a new DL classification framework is proposed in this study to overcome the overfitting problem by transfer learning and learning-based data augmentation technique (i.e., AutoAugment). Furthermore, we also employ class activation map (CAM) visualization technique to help understand how the CNN classifiers work to abstract patterns from the input. Specifically, due to a lack of architectural style datasets for the CTSs, a new annotated dataset is first established with six representative classes. Second, several representative CNNs are leveraged to benchmark the new dataset. Third, to address the overfitting problem of the CNNs, a new DL framework is proposed which combines transfer learning and AutoAugment to improve the classification performance. Extensive experiments are conducted on the new dataset to demonstrate the effectiveness of our framework. The proposed framework achieves much better performance than baselines, greatly mitigating the overfitting problem. Additionally, the CAM visualization technique is harnessed to explain what and how the CNN classifiers implicitly learn for recognizing a specified architectural style.
摘要：
菜地土壤重金属污染不仅影响蔬菜质量还对人体产生潜在的健康风险。本文以衡阳市三大工业园区周边菜地土壤为研究对象，通过分析Cu、Zn、Pb、Mn和Hg五种重金属的含量特征，并利用地累积指数、潜在生态风险指数和人体健康风险指数法对其污染现状及潜在风险进行评价。结果表明：（1）以土壤环境质量农用地土壤污染风险管控值为标准，衡阳市三大工业园区周边菜地土壤中重金属Cu、Pb、Zn含量超过风险管控值的点位为68%、66%、10%，其中在Cu和Pb超标点位中白沙洲工业园区周边菜地分别占76.5%和50%,Mn和Hg尚未超标。（2）通过地累积指数评价，白沙洲工业园区周围菜地土壤中Zn呈偏中度污染，Hg、Cu轻度污染；松木工业园区周围菜地土壤中Zn呈偏中度污染，Pb、Hg轻度污染；衡钢产业区周围菜地土壤中Cu、Zn、Hg存在轻度污染。（3）潜在生态风险指数（RI）显示三大工业园区菜地土壤5种重金属RI贡献大小依次为：RI （松木工业园区）＞ RI （衡钢产业区）＞ RI （白沙洲工业园区），这三个工业园区的菜地土壤属于轻微生态危害。（4）研究区土壤中5种重金属目前均不会对成人和儿童造成非致癌风险。衡阳市三大工业园区周边菜地虽部分已受到了一定的污染，但其致癌风险可以忽略。
