Spatial distribution of rodent pests in desert forest based on UAV remote sensing

Wen, Amin; Zheng, Jianhua; Chen, Meng; Mu, Chen; Ma, Tao

doi:10.1109/igarss.2016.7729463

Cited by 6 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of forest health monitoring and disease/pest detection there is a clear indication that rotary-wing UAVs are the most used, following the tendency of the previous discussed studies. In the reviewed studies only two employed fixed-wing UAVs [148,151]. From sensing payload stand of view, RGB sensors were the most used.…”

Section: Forest Health Monitoring and Disease Detectionmentioning

confidence: 99%

“…Näsi et al [140,149] used an hyperspectral sensor as main sensor with the addition of a RGB sensor, while Smigaj et al [148] used a TIR sensor along with RGB and CIR sensors and Minařík and Langhammer [141] used a multispectral sensor. Wen et al [151] aimed to detect pest infestations, more specifically rodent infestations. Minařík and Langhammer [141] assessed forest disturbances such as windstorms and bark beetle outbreaks.…”

Section: Forest Health Monitoring and Disease Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

View full text Add to dashboard Cite

Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

show abstract

Section: Forest Health Monitoring and Disease Detectionmentioning

confidence: 99%

Section: Forest Health Monitoring and Disease Detectionmentioning

confidence: 99%

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Due to the use of a monthly temporal resolution, the effects of temporal (i.e., interannual, seasonal) variability of the vegetation condition on RHD may have been considered to be a greater influence than the spatial variability. Furthermore, the 90 m resolution of terrain variables may not be a fine enough scale to capture the impacts of small-scale terrain factors (e.g., sand dunes with a scale below 20 m) on RHD [4]. In this case, the small-scale terrain factors obtained by UAV remote sensing [4] can be value-added because the extracted values from shady slopes and sunny slopes can be more accurate.…”

Section: Uncertainties and Limitations Concerning The Driving Environmental Mechanisms Of Rhd In Ntxjmentioning

confidence: 99%

“…In NTXJ, rodent damage mainly occurs in grasslands and deserts and is primarily caused by Lagurus luteus (LL), which is mainly distributed in low mountain grassland, and Rhombomys opimus (RO), which is mainly distributed in the low desert. While the spatial and temporal distributions of RHD for both of these species are influenced by natural factors such as terrain [4], precipitation [5], temperature [6], soil texture [7,8], and vegetation [9], they may also be influenced by anthropogenic factors in the area, such as sheep grazing [10,11]. However, The temporal and spatial distribution of RHD and its complex driving mechanism remain uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…However, The temporal and spatial distribution of RHD and its complex driving mechanism remain uncertain. Although some ground in situ investigation and UAV-based approaches [4,12] have been used to measure the local or small-scale RHD within NTXJ and to analyze the relationships between RHD and environmental variables, there are numerous difficulties in extrapolating these local relationships to large-scale spatiotemporal mapping. The relationships between RHD and environmental variables' data may vary by scale, which, for example, may be caused by the difference in the vegetation information provided at the field-scale of local studies and the pixel-scale of satellite remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the Impacts of Environmental Factors on Rat Hole Density in the Northern Slope of the Tienshan Mountains with Satellite Remote Sensing Data

et al. 2021

View full text Add to dashboard Cite

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems.

show abstract

Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes

Liu,

Xu,

Zheng

et al. 2024

Ecology and Evolution

View full text Add to dashboard Cite

The great gerbil (Rhombomys opimus) is a gregarious rodent in Central Asia and is one of the major pests found in desert forest and grassland areas. The distribution changes and migration routes of R. opimus in Central Asia under climate change remain unexplored. This study employed multi‐model ensemble, correlation analysis, jackknife method, and minimum cumulative resistance (MCR) model to simulate the potential habitat of R. opimus under current and future (2030 and 2050) climate scenarios and estimate its possible migration routes. The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. The model predicted the potential distribution of R. opimus in Central Asia with an area under the curve (AUC) of 0.986 and a True Skill Statistic (TSS) of 0.899, demonstrating excellent statistical accuracy and spatial performance. Under future climate scenarios, northern Xinjiang and southeastern Kazakhstan will remain the core areas of R. opimus distribution. However, the optimal habitat region will expand relative to the current one. This expansion will increase with the rising CO2 emission levels and over time, potentially enlarging the suitable area by up to 39.49 × 104 km2. In terms of spatial distribution, the suitable habitat for R. opimus is shifting toward higher latitudes and elevations. For specific migration routes, R. opimus tends to favor paths through farmland and grassland. This study can provide guidance for managing and controlling R. opimus under future climate change scenarios.

show abstract

Spatial distribution of rodent pests in desert forest based on UAV remote sensing

Cited by 6 publications

References 3 publications

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Analysis of the Impacts of Environmental Factors on Rat Hole Density in the Northern Slope of the Tienshan Mountains with Satellite Remote Sensing Data

Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes

Contact Info

Product

Resources

About