Abstract

Greenhouse detection is important with respect to urban and rural planning, yield estimation and crop planning, sustainable development, natural resource management, and risk analysis and damage assessment. The aim of this study is to detect greenhouse areas by using color and infrared orthophoto (RGB-NIR), topographic map, and Digital Surface Model (DSM) approaches. The study was implemented in the Kumluca district of Antalya, Turkey, which includes intensive greenhouse areas. In this study, color and infrared orthophotos, a normalized Digital Surface Model (nDSM), Normalized Difference Vegetation Index (NDVI), and Visible Red-Based Built-Up Index (VrNIR-BI) were used, and the greenhouse areas were detected using an Object-Based Image Analysis (OBIA). In this process, the optimum scale parameter was determined automatically by the Estimation of Scale Parameter2 (ESP2) tool and Multi Resolution Segmentation (MRS) was used as the segmentation algorithm. In the classification stage, K-Nearest Neighbor (K-NN), Random Forest (RF), and Support Vector Machine (SVM) classification techniques were used, and the accuracies of the classification results were compared. The obtained results demonstrated that greenhouse areas can be determined from color and infrared orthophoto and DSM data successfully by using the OBIA. The highest overall accuracy was obtained when the SVM classifier was used, with 94.80%.

Highlights

  • Greenhouse extraction is important area of research, and creating and updating greenhouse information systems is vital for urban and rural planning, yield estimation, crop planning, and risk analysis and damage assessment in case of natural disaster

  • The obtained classification results indicate the success of the ObjectBased Image Analysis (OBIA) for greenhouse detection

  • Greenhouse areas were obtained from color and infrared orthophoto and normalized Digital Surface Model (nDSM)

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Summary

Introduction

Greenhouse extraction is important area of research, and creating and updating greenhouse information systems is vital for urban and rural planning, yield estimation, crop planning, and risk analysis and damage assessment in case of natural disaster. The fast and accurate detection of greenhouses automatically from remote sensing imagery saves labor and time. With the development of digital image analysis and processing methods, the greenhouse detection process has become easier and faster when compared with traditional techniques. After examining studies about greenhouse detection from remotely sensed data, it can be stated that the classification techniques are widely used. The most widely used classification technique for greenhouse detection found in the literature is the Maximum Likelihood Classification. Carvajal et al [1,2] used the Artificial Neural

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