A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

Abstract

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.

Exploring Capabilities of Sentinel-2 for Vegetation Mapping Using Random Forest

“…The Agricultural Monitoring Community of Practice of the Group on Earth Observations (GEO), with its Integrated Global Observing Strategy (IGOL), also calls for an operational system in order to monitor the global agriculture using remote sensing (Belgiu, 2018). In literature, there are many studies for Land Use Land Cover classification as well some of them are dedicated to vegetation mapping used various supervised and unsupervised algorithms in pixel based or object based frameworks (Belgiu, 2018;Chuang, 2016;Nay, 2018;Colkesen, 2017;Li, 2014). A meta-analysis on supervised pixel based techniques for land cover classification performed by Khatami et.al (2016) reveals that inclusion of ancillary data, texture, multi-angle and temporal images gives significant improvement in accuracy of classification.…”

“…Belgiu (2018) used time-weighted dynamic time warping (TWDTW) method for crop land mapping on time series Sentinel-2 data by adopting pixel based and object based classification by considering three different study areas and concludes that object-based classification give better results than pixel-based approach. Another study for tea crop mapping has been carried by (Chuang, 2016) using WorldView-2 imagery and machine learning techniques (RF and SVM) and results show that highest overall accuracy is achieved in OBIA. Nay et al (2018) applied machine learning techniques for forecasting vegetation health by using (MODIS) data sets.…”

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

“…This matrix includes the relative frequencies with which two neighbor pixels, separated at distance d with direction a, occur in the image, one with a grey tone i, and other with a grey tone j. The set of features proposed by Haralick et al [16] based on GLCM has been recently assessed for diverse applications using very high resolution (VHR) imagery such as GeoEye [25,26], WorldView-2 [27][28][29][30], WorldView-3 [31] or Unmanned Aerial Vehicles (UAVs) imagery [32,33]. GLCM based features (H) have been successfully used in the agroforestry field for diverse applications as land cover mapping of forested areas [24] and for crops identification in agriculture [3,34], such as vineyards and orchards [4,[34][35][36][37].…”

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

“…Karlson et al [28] used WV-2 imagery to map tree crown in managed woodlands. Chuang and Shiu [29] used WV-2 pan-sharpened imagery to map tea crop. WV-2 has shown advantages in classifying bamboo patches, as well.…”