Wildlife monitoring | ScienceGate

Continued international integration of the post-Deng era (1990s on) transformed panda country. The specific site of the Wanglang reserve became a juncture where the local Baima villagers, international scientists, NGOs, and tourists (both foreign and domestic) competed to define the giant panda’s place in the environment and in China. Persistently pursuing its charter purposes as a scientific research base, the Wanglang reserve becomes a model and training station for wildlife monitoring and experimental conservation. One experiment, ecotourism has a dramatic impact on the area. The colorful ethnic character of the Baima people initially proved to be an asset to World Wide Fund for Nature (WWF) efforts to instigate tourism. The industry took on an identity independent of panda preservation, leading reserve staff to reemphasize Wanglang’s ties to science.

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Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area

Future Internet ◽

10.3390/fi13050115 ◽

2021 ◽

Vol 13 (5) ◽

pp. 115

Author(s):

Mike Oluwatayo Ojo ◽

Davide Adami ◽

Stefano Giordano

Keyword(s):

Monitoring Network ◽

Forest Vegetation ◽

Delivery Ratio ◽

Communication Link ◽

Wildlife Monitoring ◽

Design Development ◽

Sustainable Solutions ◽

Iot Applications ◽

Dense Forest ◽

Smart Agriculture

Smart agriculture and wildlife monitoring are one of the recent trends of Internet of Things (IoT) applications, which are evolving in providing sustainable solutions from producers. This article details the design, development and assessment of a wildlife monitoring application for IoT animal repelling devices that is able to cover large areas, thanks to the low power wide area networks (LPWAN), which bridge the gap between cellular technologies and short range wireless technologies. LoRa, the global de-facto LPWAN, continues to attract attention given its open specification and ready availability of off-the-shelf hardware, with claims of several kilometers of range in harsh challenging environments. At first, this article presents a survey of the LPWAN for smart agriculture applications. We proceed to evaluate the performance of LoRa transmission technology operating in the 433 MHz and 868 MHz bands, aimed at wildlife monitoring in a forest vegetation area. To characterize the communication link, we mainly use the signal-to-noise ratio (SNR), received signal strength indicator (RSSI) and packet delivery ratio (PDR). Findings from this study show that achievable performance can greatly vary between the 433 MHz and 868 MHz bands, and prompt caution is required when taking numbers at face value, as this can have implications for IoT applications. In addition, our results show that the link reaches up to 860 m in the highly dense forest vegetation environment, while in the not so dense forest vegetation environment, it reaches up to 2050 m.

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Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory

Applied Sciences ◽

10.3390/app11094070 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4070

Author(s):

Rabiul Hasan Kabir ◽

Kooktae Lee

Keyword(s):

Detection Rate ◽

Transport Theory ◽

Optimal Transport ◽

Position Estimation ◽

Wildlife Monitoring ◽

High Detection Rate ◽

Monitoring Strategies ◽

Spatio Temporal ◽

Optimal Transport Theory ◽

Multi Uav

This paper addresses a wildlife monitoring problem using a team of unmanned aerial vehicles (UAVs) with the optimal transport theory. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work. In all different simulations, it is shown that the proposed scheme significantly outperforms other UAV-based wildlife monitoring strategies in terms of the target detection rate up to 3.6 times.

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