Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

Abstract

The use of geospatial sciences and technologies for the management of grazinglands has fostered a plethora of applications related to ecology, wildlife, vegetation science, forage productivity and quality, and animal husbandry. Some of the earliest use of remote sensing dates to the proliferation of aerial photography in the 1930s. Today, remote sensing using satellite imagery, global navigation satellite systems (GNSS), and internet-connected devices and sensors allow for real- and near real-time modeling and observation of grazingland resources. In this special issue of Remote Sensing, we introduce nine original publications focusing on varying aspects of grazingland management, such as animal health and telemetry, climate change, soil moisture, herbaceous biomass, and vegetation phenology. The work in this issue spans a diverse range of scale from satellite to unmanned aerial systems imagery, as well as ground-based measurements from mounted cameras, telemetry devices, and datalogging devices. Remote sensing-based technologies continue to evolve, allowing us to address critical issues facing grazingland management such as climate change, restoration, forage abundance and quality, and animal behavior, production, and welfare.

A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices

“…The size of input cache and output cache is calculated by Equations ( 4) and (5). The total delay time for completing a convolution operation with or without the dual cache design is shown in Equations ( 6) and (7).…”

“…The above-described method is capable of increasing the local memory bandwidth, and it can be combined with the dual cache design to enhance the performance of the system's ability to read and write data. The total delay time for completing a convolution operation with or without the dual cache design is shown in Equations ( 6) and (7).…”

“…Thanks to the development of deep learning and computer vision over the past few years, numerous high-precision object detection models based on convolutional neural networks (CNNs) have been proposed for remote sensing image processing, which has notably improved the efficiency of feature extraction from remote sensing images, and object detection from remote sensing images based on deep learning has also developed rapidly. It has been extensively employed in ship detection [ 1 , 2 , 3 ], meteorological environment detection [ 4 , 5 ], husbandry monitoring [ 6 , 7 ], road extraction [ 8 , 9 , 10 ], and other fields.…”

A changing river: Long‐term changes of sinuosity and land cover in the Navasota River Watershed, Texas

“…Remote sensing and Geographical Information Systems (GIS) have been deployed extensively to study land use change and water resources (Al‐Husban, 2018; Allen & Pavelsky, 2018; Berg, Popescu, et al, 2016; Berg, Wilcox, et al, 2016; Donovan et al, 2021; Ibitoye, 2021; Rhodes et al, 2021). The launch of the Landsat satellite program in 1972 was a major milestone and marked a new generation of earth observation (Booth & Tueller, 2003; Gorelick et al, 2017; Markham et al, 2004; Rhodes et al, 2022). One obvious downside to the satellite era is that the earliest imagery is at a coarse resolution and the temporal history is still rather limited.…”

“…One obvious downside to the satellite era is that the earliest imagery is at a coarse resolution and the temporal history is still rather limited. However, historical aerial photography began to proliferate in government and private collections following World War I, providing a valuable resource for monitoring land use change and natural resources (Lillesand et al, 2015; Rhodes et al, 2022). Though limited in spectral bandwidth, these data sets serve as a crucial bridge between the pre‐ and postsatellite era.…”