Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping
| Autor: | Robert De Wulf, Frederick N. Numbisi, Frieke Van Coillie |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
Agriculture and Food Sciences 010504 meteorology & atmospheric sciences Cloud cover Geography Planning and Development Multispectral image 0211 other engineering and technologies mapping cocoa agroforests lcsh:G1-922 02 engineering and technology classification uncertainty CLASSIFICATION ACCURACY 01 natural sciences GLCM textures Image texture SYSTEMS random forest algorithm DRIVERS Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Mathematics Tropical deforestation grey level quantization VEGETATION INDEXES Congo Basin rainforest FOREST FRAMEWORK COVER Random forest machine learning Earth and Environmental Sciences SAMPLE-SIZE Grey level Sentinel-1 DEFORESTATION CARBON STOCKS Cropping lcsh:Geography (General) SAR |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 8, Iss 4, p 179 (2019) ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION ISPRS International Journal of Geo-Information Volume 8 Issue 4 |
| ISSN: | 2220-9964 |
| Popis: | Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification&rsquo s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories. |
| Databáze: | OpenAIRE |
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