Microcopter-based colour infrared (CIR) close-range remote sensing as a subsidiary tool for precision farming
| Autor: | Knoth, Christian, Prinz, Torsten, Loef, Peter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| DOI: | 10.5880/tr32db.kga92.7 |
| Popis: | Microcopters as a highly flexible and low-cost sensor platform provide new opportunities of data acquisition for various environmental and geoscientific purposes (e.g. environmental monitoring, forestry, geospatial data etc.). One promising field of application for this technique is precision farming. Thereby, the application of capital equipment like crop protection products (also fertilizer) can be adapted to the spatial and temporal dynamics of soil and population parameters to reduce costs and keep processes more environmentally-compatible. In this context, the objective of this project is to produce CIR photographs and other remote sensing products in the visible (VIS) and near infrared (NIR) range as classified input data for subsequent procedures of precision farming and for efficiency tests in a user-defined spatial and temporal resolution. Therefore a remotely controlled microcopter has been equipped with a modified compact digital camera now capable of taking images not only in the VIS but also in the ultraviolet (UV) and NIR spectra (about 320 nm to 1100 nm), depending on the applied optical filters. The aerial surveys are conducted with a microcopter which is capable of autonomously completing a GPS waypoint track specified by the user. The localizations of exposures, height above surface, the camera heading, and other parameters can be set up in advance using a flight software. The microcopter itself represents an ultraflexible multi-sensor platform, where the camera provides a modular setup for generating high-resolution aerial CIR photographs. The images obtained from the surveys are being rectified and subjected to object orientated texture analysis for supervised classifications regarding surface anomalies like albedo variations of green vegetation. The results are processed to generate accurate position data for the distinction of various vegetation types like weed and crop, or different states of vegetation health due to soil dryness, precipitation damages or pest infestation. Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling, 18-19 November 2010, University of Cologne, Germany, K��lner Geographische Arbeiten, 92, pp. 49-54 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|