期刊:
Science of The Total Environment,2021年774:145067 ISSN:0048-9697
通讯作者:
Jiping Li
作者机构:
[Tang, Tao; Li, Jiping; Sun, Hua] Cent South Univ Forestry & Technol, Fac Forestry, 498 Southern Shaoshan Rd, Changsha 410004, Hunan, Peoples R China.;[Tang, Tao; Li, Jiping; Sun, Hua] State Forestry Adm Forest Resources Management &, Key Lab, Changsha 410004, Peoples R China.;[Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Deng, Chao] Forestry Bur Changde City Hunan Prov, Changde 415099, Peoples R China.
通讯机构:
[Jiping Li] F;Faculty of Forestry, Central South University of Forestry and Technology, Changsha 410004, Hunan Province, China<&wdkj&>Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
摘要:
The decline and fragmentation of habitats areas are two main factors that lead to the reduction of biodiversity in landscape ecosystems. As a kind of large carnivores, South China tiger (Panthera tigris amoyensis) is one of the most endangered tiger subspecies and considered to be extinct in the wild. The Chinese government has intended to release a certain number of tigers into two of their historically habitats areas, Hupingshan-Houhe national nature reserves (NNR) in central-south China that provides suitable habitats for P. tigris. Because wild boar (Sus scrofa) is a prey of P. tigris, spatially characterizing the populations of the prey and its habitats is critical for the success of habituating the tigers to the areas. Although there has been effort made to protect the habitats of wild boar, there have been no report that deal with investigation and analysis of the habitat suitability and potential forwild boar, especially in terms of landscape connectivity. Herewe present the novel integration of the habitat suitability index (HSI) and graph-based network to identify the priority areas for wild boar dispersal in and around the NNR. In addition, a novel method to identify the proper connectivity distance to avoid excessive connectivity when the field data are essentially non-existent. Results showed that in summer andwinter, the potential habitat areas were 6848-10,245 and 5984-10,152km(2), respectively. The total area of the priority patches was 1590 km(2), approximately occupying 16% of the suitable habitat area. Our study indicated that the novel integration of the HSI and network analysis led to an effective approach to spatially characterize priority patches to support decision-making for landscape planning. The results shown here also have implications for future efforts for habituating large carnivores into their historical habitat regions. (c) 2021 Elsevier B.V. All rights reserved.
作者机构:
[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing; Wang, Qing] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.;[Luo, Peng] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.
通讯机构:
[Wang, Guangxing] C;[Wang, Guangxing] K;[Wang, Guangxing] S;Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.
关键词:
land degradation;optimized k-nearest neighbors;landsat image;percentage vegetation cover;Duolun County;Kangbao County
摘要:
Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.