作者机构：
[Fan, Xuejiao; Deng, Zhiwei; Liu, Jianxiong; Quan, Bin] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;[Quan, Bin] Hunan Prov Collaborat Innovat Ctr Digital Heritag, Hengyang 421002, Peoples R China.;[Quan, Bin] Hengyang Normal Univ, HIST Hengyang Base, Hengyang 421002, Peoples R China.
通讯机构：
[Bin Quan] C;College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Collaborative Innovation Center for Digital Heritage of Ancient Village and Town Cultural Heritage, Hengyang 421002, China<&wdkj&>HIST Hengyang Base, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
land use change;intensity analysis;balance of cultivated land occupation and compensation;Changsha–Zhuzhou–Xiangtan
摘要：
The Changsha-Zhuzhou-Xiangtan region has experienced rapid social and economic development over the past 40 years, and cultivated land has changed dramatically. The contradiction between built and cultivated land has intensified, for which the local government has implemented a series of policies related to cultivated land protection. However, thus far, it is not clear what the substantial effects of the cultivated land protection policies are. To this end, this paper quantitatively characterizes the changes in the Changsha-Zhuzhou-Xiangtan region during the 20 years before and after the implementation of the cultivated land occupation balance policy, based on land use data from 1980, 2000, and 2020 using intensity analysis. In this paper, we examine the types of spatial land use patterns occurring in Changsha-Zhuzhou-Xiangtan since 1980 and explore the transition path of land use types in urban-rural integration. After the cultivated land protection policy, the transformation relationship between land use types and the changing trend of the cultivated land area was analyzed from the landscape scale. The influence of policy factors on the transformation of land use types was revealed. The results show that, from 1980 to 2020, the changing intensity of construction land and unused land was relatively large and was in an active state; the amount of built land in the Changsha-Zhuzhou-Xiangtan region has been growing, with a net increase of 1101 km(2), while the amount of cultivated land has been showing a net decrease, with a net reduction of 677 km(2). Moreover, the cultivated land has mainly been converted into built land, and the lost cultivated land area in Changsha-Zhuzhou-Xiangtan has not been fully compensated elsewhere in the region, indicating that the cultivated land protection policy has not been able to maintain the cultivated land area in Changsha-Zhuzhou-Xiangtan. From 2000 to 2020, cultivated land change was mainly due to exchange, which indicates that the policy has had a particular effect on the protection of cultivated land. Still, if the government wants to achieve the "balance of cultivated land occupation and compensation" goal, it must establish a complete system for the allocation of cultivated land resources. This study can provide a scientific reference for further implementing the cultivated land protection policy, which is thus of great significance for promoting the construction of the Changsha-Zhuzhou-Xiangtan region and its high-quality economic and social development.
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.
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：
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, 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.
