Abstract
Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML), Random Forest (RF), and Support Vector Machines (SVM) classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.
Highlights
Determination of existing greenhouse areas is very important for urban and rural planning, sustainable development, yield estimation and planning and avoiding environmental problems related to greenhouse expansion (Agüera and Liu, 2009; KocSan, 2013a; Tasdemir and Koc-San, 2014)
The confusion matrices of Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machines (SVM) classifiers are given in Tables 1, 2, and 3, respectively
When the obtained results were analysed, it can be stated that all three classification techniques provide quite successful results for plastic and glass greenhouse detection from WorldView-2 satellite imagery
Summary
Determination of existing greenhouse areas is very important for urban and rural planning, sustainable development, yield estimation and planning and avoiding environmental problems related to greenhouse expansion (Agüera and Liu, 2009; KocSan, 2013a; Tasdemir and Koc-San, 2014). The biggest problem in Geographic Information Systems applications is the lack of up-to-date data. This problem can be resolved by using remotely sensed data. With the recent technological developments, the high resolution satellite images have become important data sources that can be used for obtaining and updating man-made objects and geographical data rapidly and effectively, for mapping applications and urban planning (Muyanga et al, 2007; Koc-San and Turker; 2012, Liu et al, 2015, Wang et al, 2015). The high resolution satellite images can be used to monitor the current situation and expansion of the greenhouse areas efficiently (Agüera and Liu, 2009; Koc-San, 2013a; Tasdemir and Koc-San, 2014)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
