Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 7; p. 1199
• Main Authors: Zhu, Yaohui, Yang, Guijun, Yang, Hao, Wu, Jintao, Lei, Lei, Zhao, Fa, Fan, Lingling, Zhao, Chunjiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Age; Agricultural production; Algorithms; apple orchard; Apples; Classification; Cluster analysis; Clustering; Cultivation; Euclidean geometry; Foliage; Forests; Fruits; Identification; Landsat; Landsat satellites; Mathematical analysis; Methods; Normalized difference vegetative index; Orchards; phenology; Pixels; Planting; planting year; Remote sensing; Satellite imagery; Satellites; Seedlings; Spatial distribution; spatiotemporal fusion; Support vector machines; Time series; Vegetation; Vegetation index; United Kingdom > UK; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12071199

The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images.