Habitat Distributions and Abundance of Four Wild Herbivores on the Qinghai–Tibetan Plateau: A Review DOI Creative Commons
Tian Qiao, Chiwei Xiao,

Zhiming Feng

и другие.

Land, Год журнала: 2024, Номер 14(1), С. 23 - 23

Опубликована: Дек. 26, 2024

Understanding the change in habitat distributions and abundance of wildlife space time is critical for conservation biodiversity mitigate human–wildlife conflicts (HWCs). Tibetan antelope or chiru (Pantholops hodgsonii), gazelle goa (Procapra picticaudata), wild ass kiang (Equus kiang), Wild yak (Bos mutus) have been sympatric on Qinghai–Tibetan plateau (QTP) numerous generations. However, reviews these four herbivores (WHs), as well methods examining changes aspects, are still lacking. Here, we firstly review major WHs QTP across different periods, underlying causes HWCs. Furthermore, critically compare three aspects methods: transect surveys, machine learning (ML), deep (DL) studying WHs. The results show that since 1990s, exhibited a trend initial decline followed by recovery, largely attributed to global climate warming decrease illegal hunting. recent years, primary challenge has shifted from protection balancing human interests within constraints limited resources. In future, should focus enhancing ecological functions habitats achieve harmonious coexistence between humans nature, establishing scientific compensation mechanism conflicts. order accurately calculate changes, select appropriate models analyze based their specific characteristics environmental conditions. Additionally, with advancement large models, AI (artificial intelligence) be utilized precise rapid conservation. findings this study also provide guidance reference addressing issues related other regions globally.

Язык: Английский

Enhanced Yolov8 network with Extended Kalman Filter for wildlife detection and tracking in complex environments DOI Creative Commons

Liang-Ting Jiang,

Li Wu

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102856 - 102856

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

5
A multi-target tracking method for UAV monitoring wildlife in Qinghai DOI Creative Commons
Guoqing Zhang, Wei Luo,

Quanqin Shao

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0317286 - e0317286

Опубликована: Апрель 11, 2025

The Procapra przewalskii, plays a vital role in sustaining the ecological balance within its habitat, yet it faces significant threats from environmental degradation and illegal poaching activities. In response to this urgent conservation need, article proposes multi-object tracking (MOT) method for unmanned aerial vehicle (UAV). Initially, approach utilizes modified YOLOv7 network, which incorporates Group-Selective Convolution (GSConv) Neck component, effectively enhancing network’s ability preserve detailed information while simultaneously reducing computational load. Subsequently, Content-Aware ReAssembly of Features (CARAFE), an innovative feature upscaling method, replaces conventional nearest neighbor interpolation minimize loss critical data during image processing. phase, Deep SORT algorithm is expanded with proprietary UAV camera motion compensation (CMC) module that eliminates impact jitters. Moreover, system has incorporated confidence optimization strategy (COS) enhances performance especially when individuals are partially or fully obscured. been tested on przewalskii shown be effective. results show gains metrics where achieved improvements 7.0% MOTA, 3.7% MOTP, 5.8% IDF1 score compared traditional model. Improved methods can alleviate occlusion rapid movement tracking, thereby more accurately monitoring status each protecting it. Also, efficiency multi-target through use sufficient operational demands UAV-based wildlife monitoring, thus being reliable tool accurate efficient desired.

Язык: Английский

Процитировано

0
DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments DOI Creative Commons
Haitao Wu,

X. H. Mo,

Sijian Wen

и другие.

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(9), С. 102220 - 102220

Опубликована: Окт. 21, 2024

Язык: Английский

Процитировано

4
Advancing Sika deer detection and distance estimation through comprehensive camera calibration and distortion analysis DOI Creative Commons
Sandhya Sharma, Stefan Baar, Bishnu Prasad Gautam

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103064 - 103064

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Field-deployable real-time AI System for chemical warfare agent detection using YOLOv8 and colorimetric sensors DOI
Seok-Hyung Bae, Ku Kang,

Young Kyun Kim

и другие.

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2025, Номер unknown, С. 105365 - 105365

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Synergistic enhancement of photoluminescence and advanced deep learning model through YOLOv8x in combined effects of carbon dots and Sr₉Al₆O₁₈:Sm³⁺ phosphors DOI
B.R. Radha Krushna,

I.S. Pruthviraj,

S.C. Sharma

и другие.

Optical Materials, Год журнала: 2024, Номер unknown, С. 116455 - 116455

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

3
Nondestructive detection of surface defects of curved mosaic ceramics based on deep learning DOI
Guanping Dong, Xingchen Pan, Sai Liu

и другие.

Ceramics International, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0
Hierarchical deep learning framework for automated marine vegetation and fauna analysis using ROV video data DOI Creative Commons
Bjørn Christian Weinbach, Rajendra Akerkar,

Marianne Nilsen

и другие.

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102966 - 102966

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0
Habitat Distributions and Abundance of Four Wild Herbivores on the Qinghai–Tibetan Plateau: A Review DOI Creative Commons
Tian Qiao, Chiwei Xiao,

Zhiming Feng

и другие.

Land, Год журнала: 2024, Номер 14(1), С. 23 - 23

Опубликована: Дек. 26, 2024

Understanding the change in habitat distributions and abundance of wildlife space time is critical for conservation biodiversity mitigate human–wildlife conflicts (HWCs). Tibetan antelope or chiru (Pantholops hodgsonii), gazelle goa (Procapra picticaudata), wild ass kiang (Equus kiang), Wild yak (Bos mutus) have been sympatric on Qinghai–Tibetan plateau (QTP) numerous generations. However, reviews these four herbivores (WHs), as well methods examining changes aspects, are still lacking. Here, we firstly review major WHs QTP across different periods, underlying causes HWCs. Furthermore, critically compare three aspects methods: transect surveys, machine learning (ML), deep (DL) studying WHs. The results show that since 1990s, exhibited a trend initial decline followed by recovery, largely attributed to global climate warming decrease illegal hunting. recent years, primary challenge has shifted from protection balancing human interests within constraints limited resources. In future, should focus enhancing ecological functions habitats achieve harmonious coexistence between humans nature, establishing scientific compensation mechanism conflicts. order accurately calculate changes, select appropriate models analyze based their specific characteristics environmental conditions. Additionally, with advancement large models, AI (artificial intelligence) be utilized precise rapid conservation. findings this study also provide guidance reference addressing issues related other regions globally.

Язык: Английский

Процитировано

0