通讯机构：
[Feng, C ] H;Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.
关键词：
Event-based extreme precipitation;Time distribution pattern;Contribution rate of urbanization;Xiangjiang River Basin
摘要：
An in-depth understanding of event-based extreme precipitation (EEP), emphasizing precipitation process, can help to prevent the risk posed by regional high-intensity and persistent precipitation. The concept of time distribution pattern (TDP) is used to distinguish EEPs, which classifies EEPs according to the occurrence time of extreme precipitation. Furthermore, TDP1,2,3 is that the distribution of daily precipitation above the threshold is in the first half, in the second half, and both the first half and second half of EEP, respectively. We analyze temporal characteristics, spatial distribution, future trends of EEP, and the contribution rate of urbanization to EEP in this study. EEP thresholds exhibit a latitudinal gradient from central to northern and southern regions except for Nanyue Station (NY). TDP1 and TDP2 account for more than 60% among the total of EEPs. However, TDP3 is the dominant precipitation type observed at each station from the perspective of precipitation, intensity, duration, especially, in summer. EEP is less and TDP is unstable in autumn and winter. In general, there is an increasing trend in EEP and it is predicted that the trend of EEP will continue to rise. Moreover, the contribution rate of urbanization to EEP varies significantly, with a more pronounced inhibitory effect observed. The inhibitory effect of urbanization on the frequency and duration of TDP3 reached 60.83% and 72.77%, respectively. However, it is more significant on the extreme nature of TDP1 under urbanization, with a positive contribution rate of 9.63% and 21.83% to precipitation and intensity of TDP1, respectively. The results conclude that the higher the level of urbanization, the more pronounced the extreme trend of TDP1 becomes.
摘要：
The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.
摘要：
传统聚落集聚了大量的古代建筑和民俗等传统文化资源，以其突出的历史、文化和艺术等价值而备受关注，提取其丰富的历史文化信息并服务于现代产业发展具有积极的意义。针对当前尚缺乏从地理知识提取和表达视角对传统聚落丰富的历史文化信息进行知识抽取、组织和表达，进而实现“数据-信息-知识-智慧”转换这一问题，本文提出传统聚落景观基因本体（Geographic Ontology of Cultural Landscape Genes of Traditional Settlements, GeoOnto-CLGTS），并探索传统聚落景观基因的内在关联特征。首先，结合地理信息本体和传统聚落景观基因特征分析了GeoOnto-CLGTS的概念及表达方法，并提出GeoOnto-CLGTS模型的构建方法。其次，结合七步法的地理信息本体建模方法，梳理景观基因概念、关联关系和数据属性特征，自顶向下构建GeoOnto-CLGTS的概念层。并以123个中国传统聚落为案例，通过protégé工具进行实例补充，实现了GeoOnto-CLGTS模型的实例层构建。最后，通过Neo4j图数据库对本体数据进行存储，构建传统聚落景观基因知识图谱，实现了景观基因信息的查询。结果表明，本文构建的GeoOnto-CLGTS可以为今后开展传统聚落文化资源的知识发现及促进传统聚落的数字化保护提供有益的借鉴。
作者机构：
[Hu, Yong; Fu, Jing; Liu, Jianxiong; Yang, Liguo; Deng, Yunyuan; Su, Baoling] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang, Hunan, Peoples R China.;[Fu, Jing; Yang, Liguo; Deng, Yunyuan] Hengyang Normal Univ, Hunan Natl Local Joint Engn Lab Digital Preservat, Hengyang, Hunan, Peoples R China.;[Fu, Jing; Qin, Jianxin] Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha, Hunan, Peoples R China.;[Fu, Jing; Yang, Liguo; Deng, Yunyuan] UNESCO, Int Ctr Space Technol Nat & Cultural Heritage HIST, Hengyang Base, Hengyang, Hunan, Peoples R China.;[Zhang, Zhongbo] Hunan Weather Modificat Off, Changsha, Hunan, Peoples R China.
通讯机构：
[Fu, J ] H;Hengyang Normal Univ, Coll Geog & Tourism, Hengyang, Hunan, Peoples R China.;Hengyang Normal Univ, Hunan Natl Local Joint Engn Lab Digital Preservat, Hengyang, Hunan, Peoples R China.;Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha, Hunan, Peoples R China.;UNESCO, Int Ctr Space Technol Nat & Cultural Heritage HIST, Hengyang Base, Hengyang, Hunan, Peoples R China.
摘要：
<jats:title>Abstract</jats:title><jats:p>Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year<jats:sup>−1</jats:sup> (<jats:italic>p</jats:italic> < 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.</jats:p>
摘要：
<jats:title>Abstract</jats:title><jats:p>Understanding how resilient forests are to ecological engineering projects (EEPs) is essential to forest management and ecosystem health. Despite growing evidence that EEPs achieve increasing carbon stocks, whether such benefits can be sustainable and what are the consequences of EEPs on forest health remain unclear. This study aimed to investigate the long‐term effects of EEPs using forest resilience from aspects of resistance and recovery, by applying a change detection algorithm (breaks for additive seasonal and trend; BFAST) spatially on net ecosystem production (NEP) (proxy for carbon stocks) time series (1981–2019) in red soil hilly region (RSHR) of subtropical China. The spatial parameters (e.g., the number, magnitude, and time of changes) used to construct resilience metrics were generated based on BFAST‐derived breakpoints. These metrics were then utilized to analyze the dynamics of forest resilience in relation to EEPs factors in terms of plantation area, forest type, and stand age. Our results observed 92.77% of breakpoints in NEP after 2000, which corresponds well with the periods that multiple EEPs were conducted. NEP resilience showed great variability during 2001–2019, with a positive increasing trend in resistance (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.72) and a continuous decline (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.37) in recovery, indicating an unhealthy ecosystem in RSHR. Our findings revealed that forest resistance was strongly associated with plantation area (<jats:italic>R =</jats:italic> 0.71), and the presence of monoculture and young coniferous forest may be the potential factors for the decline in recovery. This suggested that forest resilience in RSHR is mainly modulated by large‐scale EEPs.</jats:p>
