Maize field area detection in East Java, Indonesia: An integrated multispectral remote sensing and machine learning approach
| Autor: | Arif Handoyo Marsuhandi, Arie Wahyu Wijayanto, Dwi Wahyu Triscowati |
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| Rok vydání: | 2020 |
| Předmět: |
Ground truth
010504 meteorology & atmospheric sciences Java Computer science Multispectral image 0211 other engineering and technologies 02 engineering and technology Enhanced vegetation index 01 natural sciences Ensemble learning Normalized Difference Vegetation Index Multispectral pattern recognition Random forest computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences computer.programming_language Remote sensing |
| Zdroj: | 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). |
| DOI: | 10.1109/icitee49829.2020.9271683 |
| Popis: | An accurate and high quality of agricultural monitoring and statistics commonly requires a huge amount of resources in terms of human, cost, and time. In this paper, we introduce a cost-efficient, scalable, and accurate framework for multilabel classification of the maize (corn) field area using remote sensing approaches. Official statistical survey results are used to provide the ground truth labels. Five vegetation indices, which include the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and visible atmospherically resistant index (VARI), are used to enhance the multitemporal features and predictor variables. We train an ensemble machine learning model, random forest (RF) as the classifier. Experiments are carried out to detect maize field areas in ten regencies of East Java, Indonesia using multispectral imagery data acquired by Landsat-8, Sentinel-1, and Sentinel-2 satellites. The results show that our proposed approach gains a promising accuracy of up to 87 percent in detecting maize field area. We believe that our framework could be beneficial to support and improve the quality of official statistics in the agricultural sector. |
| Databáze: | OpenAIRE |
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