Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

Use of unmanned aircraft systems (UAS) and multispectral imagery for quantifying agricultural areas damaged by wild pigs

Wild pigs (Sus scrofa) cause extensive damage to agricultural crops, resulting in lost production and income. A major challenge associated with assessing damage to crops is locating and quantifying damaged areas within agricultural fields. We evaluated a novel method using multispectral high-resolution aerial imagery, collected from sensors mounted on unmanned aircraft systems (UAS), and feature extraction techniques to detect and map areas of corn fields damaged by wild pigs in southern Missouri, USA. Damaged areas were extracted from orthomosaics using visible and near-infrared band combinations, an object-based classification approach, and hierarchical learning cycles. To validate estimates we also collected ground reference data immediately following flights. Overall accuracy of damage estimates to corn fields were similar among band combinations evaluated, ranging from 74% to 98% when using visible and near-infrared information, compared to 72%–94% with visible information alone. By including near-infrared with visible information, though, we found higher average kappa values (0.76) than with visible information (0.60) alone. We demonstrated that UAS are an appropriate platform for collecting high-resolution multispectral imagery of corn fields and that object-oriented classifiers can be effectively used to delineate areas damaged by wild pigs. The proposed approach outlines a new monitoring technique that can efficiently estimate damage to entire corn fields caused by wild pigs and also has potential to be applied to other crop types

The Use of Drones in Agricultural Production

The drones called as mainly unmanned aerial vehicles (UAVs) have been commonly used recently in agricultural production in all part of the world because of reducing costs of hardware and the software technology as well as tremendous progresses. Moreover, UAVs gave opportunities such as reaching much faster and efficient in emergency situations, allowing access to places which humans cant reach etc. Therefore, UAVs are used in many part of our life not only for agriculture both also traffic surveillance, military operations, disaster management, border-patrolling, aerial image georeferencing, courier services, firefighting as well as monitoring of wildlife, nature, sky life etc. In the agriculture, the UAVs are used mostly for monitoring the crop production using spectral imaging on each period of time in order to identify the problems on the field such as water shortage and diseases, tracking animals using cameras and herding them with creating sounds produced by the UAVs, spraying to the field with pesticide, fungicide and water by equipping spraying kit on a UAV, generating the strong winds by the propellers of the UAV increasing pollination in the hybrid plant production as well as separating the small harmful bugs from the plants etc. The UAVs contribute a lot more to the agricultural sector, if the right implementations and researches are done. However, using new implemented lightweight materials to increase the endurance of the UAV, developing new type of lenses and sensors which can identify other diseases on plants or animals which cant be seen by the current equipment and equipping a granule spreader on a UAV so that it can distribute the seeds on the field much faster than a tractor

White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward