Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

نویسندگان

چکیده

Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services management costs. To understand the patterns severity these stressors across complex landscapes, there must be an increase reliable data at scales compatible with actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate capability UAS multispectral imagery freely available National Agricultural Imagery Program (NAIP) differentiating coniferous healthy, stressed, deciduous degraded trees throughout complex, mixed-species forests. These methods are first compared assessments crown vigor field, potential supplementing resource intensive practice. This investigation uses random forest support vector machine (SVM) learning algorithms classify into five health classes. Using classifier, correctly classified Health classes overall accuracy 65.43%. similar methods, high-resolution airborne NAIP achieved 50.50% classes, reduction 14.93%. When were generalized trees, improved 71.19%, using imagery, 70.62%, imagery. Further analysis precise calibration refinement image segmentation fusion more widely distributed remotely sensed would further enhance effectively efficiently collect information from instead field methods.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13234873