Wilderness Mapping in Northeast China Based on Deep Learning

Zhang, Ping; Zhong, Qinluo; Li, Minghui; Wang, Hao; Cui, Jiaxuan

doi:10.1109/igarss46834.2022.9884671

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium 2022

DOI: 10.1109/igarss46834.2022.9884671

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Wilderness Mapping in Northeast China Based on Deep Learning

Ping Zhang

Qinluo Zhong

Minghui Li

et al.

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2024

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Spatial Identification and Conservation Gaps of Wilderness Areas in the State-Owned Forest Region of Daxing’anling

Wang,

Fu,

Yan

et al. 2024

Diversity

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Wilderness areas are important in maintaining biodiversity, and “retaining existing intact and wilderness areas” is listed as the first of 21 action targets for 2030 in the first draft of the post-2020 Global Biodiversity Framework. However, the quantity and quality of regional-scale wilderness mapping and identification of priority areas for conservation are still insufficient. Here, an approach integrating Boolean and Multi-Criteria Evaluation is used to conduct wilderness mapping, and priority conservation areas are identified based on ecosystem service values and the current status of wilderness protection in protected areas in the state-owned forest region of Daxing’anling. It is found that the wilderness areas account for 96.72% of the total area of the study area, of which high-quality wilderness areas constitute for 50.34% of the total area of wilderness areas, and the quality of wilderness is greatly affected by the railroads. Secondly, wilderness areas have higher ecosystem service values per square kilometer than non-wilderness areas, except for food supply service. In addition, 72.26% of the wilderness areas are not protected, and 52.26% of these high-quality wilderness areas with high ecosystem service values still have conservation gaps. This study can provide data support and scientific reference for future wilderness conservation works in different regions.

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Spatial Identification and Conservation Gaps of Wilderness Areas in the State-Owned Forest Region of Daxing’anling

Wang,

Fu,

Yan

et al. 2024

Diversity

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show abstract

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Wilderness Mapping in Northeast China Based on Deep Learning

Cited by 1 publication

References 8 publications

Spatial Identification and Conservation Gaps of Wilderness Areas in the State-Owned Forest Region of Daxing’anling

Spatial Identification and Conservation Gaps of Wilderness Areas in the State-Owned Forest Region of Daxing’anling

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