Innovations to expand drone data collection and analysis for rangeland monitoring

In adaptive management of rangelands, monitoring is the vital link that connects management actions with on-the-ground changes. Traditional field monitoring methods can provide detailed information for assessing the health of rangelands, but cost often limits monitoring locations to a few key areas or random plots. Remotely sensed imagery, and drone-based imagery in particular, can observe larger areas than field methods while retaining high enough spatial resolution to estimate many rangeland indicators of interest. However, the geographic extent of drone imagery products is often limited to a few hectares (for resolution ≤ 1 cm) due to image collection and processing constraints. Overcoming these limitations would allow for more extensive observations and more frequent monitoring. We developed a workflow to increase the extent and speed of acquiring, processing, and analyzing drone imagery for repeated monitoring of two common indicators of interest to rangeland managers: vegetation cover and vegetation heights. By incorporating a suite of existing technologies in drones (real-time kinematic GPS), data processing (automation with Python scripts, high performance computing), and cloud-based analysis (Google Earth Engine), we greatly increased the efficiency of collecting, analyzing, and interpreting high volumes of drone imagery for rangeland monitoring. End-to-end, our workflow took 30 days, while a workflow without these innovations was estimated to require 141 days to complete. The technology around drones and image analysis is rapidly advancing which is making high volume workflows easier to implement. Larger quantities of monitoring data will significantly improve our understanding of the impact management actions have on land processes and ecosystem traits.

The MARAS dataset, vegetation and soil characteristics of dryland rangelands across Patagonia

We present the MARAS (Environmental Monitoring of Arid and Semiarid Regions) dataset, which stores vegetation and soil data of 426 rangeland monitoring plots installed throughout Patagonia, a 624.500 km2 area of southern Argentina and Chile. Data for each monitoring plot includes basic climatic and landscape features, photographs, 500 point intercepts for vegetation cover, plant species list and biodiversity indexes, 50-m line-intercept transect for vegetation spatial pattern analysis, land function indexes drawn from 11 measures of soil surface characteristics and laboratory soil analysis (pH, conductivity, organic matter, N and texture). Monitoring plots were installed between 2007 and 2019, and are being reassessed at 5-year intervals (247 have been surveyed twice). The MARAS dataset provides a baseline from which to evaluate the impacts of climate change and changes in land use intensity in Patagonian ecosystems, which collectively constitute one of the world´s largest rangeland areas. This dataset will be of interest to scientists exploring key ecological questions such as biodiversity-ecosystem functioning relationships, plant-soil interactions and climatic controls on ecosystem structure and functioning.

Assessment of rangeland condition

There is a substantial gap in the studies on classifications of rangeland condition in the Mongolian context. To fill this gap, this study aimed at assessing the condition and changes of rangeland in the forest-steppe zone in Mongolia with the use of remote sensing technique. The Bornuur soum of Tuv aimag in Mongolia was selected as the study area. A quantitative methodology with remote sensing tool was employed to assess rangeland condition. The results of the study showed an overall accuracy of 73.9%. The study provided an insight into possible improving methodology of rangeland monitoring, sustainable land management as well as environmental studies. Бэлчээрийн төлөв байдлыг үнэлэх зарим арга Монгол орны хувьд зайнаас тандан судлалын арга технологийг ашиглан газрын бүрхэвч болон газар ашиглалтын ангилал, өөрчлөлтийг үнэлэх судалгаа түгээмэл байгаа ч тус аргыг ашиглан бэлчээрийн төлөв байдлыг үнэлсэн судалгаа ховорхон хийгдсэн байна. Иймээс ойт хээрийн бүс болох Төв аймгийн Борнуур сумын жишээгээр зайнаас тандах аргаар бэлчээрийн төлөв байдлыг n=700 цэгийн хээрийн судалгааг ашиглан үнэлсэн. Судалгааны үр дүнг хээрийн хэмжилтийн мэдээгээр үнэлэхэд таарцын үнэлгээ нь 73.9% гарсан нь цаашид бэлчээрийн төлөв байдлын судалгаанд ашиглах боломжтойг харуулж байна. Цаашид тус ангиллын аргыг улам сайжруулан бэлчээрийн төлөв байдлын үнэлгээ, мониторинг, тогтвортой газрын менежмент болон байгаль орчны судалгаанд хэрэглэж болно. Түлхүүр үг: ургамлын индекс, ландсат хиймэл дагуулын мэдээ