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

Abstract

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.

Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

“…During the first year of planting, the NDVI values of apple orchards often have the characteristics of bare land. This study draws on a previous method involving the use of remote sensing time-series data combined with vegetation cover change characteristics to determine the continuous growth years of existing apple orchards [21]-that is, if the distribution area of the existing apple orchard is determined, it is only necessary to determine the change in the distribution area of bare land in this area over time to determine the planting years of the apple trees. The method is mainly divided into three steps.…”

“…In this study, the indicators used to determine the model simulation accuracy were the coefficient of determination (R 2 ), root mean square error (RMSE), and normalized root mean square error (NRMSE), which are used to evaluate the error between the estimated value and the observed value. As the RMSE cannot effectively measure the acceptable degree of error between the measured and observed values, the NRMSE index was introduced to evaluate the accuracy of the model: NRMSE values between 0 and 10% represent a good simulation accuracy; values between 10 and 20% represent an acceptable simulation accuracy; and values higher than 20% represent an unacceptable simulation accuracy [21].…”

“…This may be due to more rainfall during this period, resulting in the growth of some herbaceous and mosses on the surface of bare land, which makes the NDVI of bare land rise rapidly. In a previous study, Zhu et al (2020) used NDVI time-series data combined with phenological characteristic parameters to classify research areas containing orchards with different species of tree and obtained an improved mapping accuracy [21]. However, in the region that is investigated in the present research (Luochuan County), only apple trees are used as a major economic orchard tree species, while other types of fruit trees are not planted over a large scale; therefore, the county's planting structure is relatively simple.…”

“…In the present study, to consider the influence of external environmental factors on fruit trees, the element of the chill accumulation of fruit trees in the early stage of flowering was added to the model. Therefore, to solve the limitations of the estimation of frost loss in orchards at a regional scale, this article combines remote sensing and meteorologically derived information based on previous research [21] (Yaohui Zhu et al, unpublished manuscript). This paper proposes a method based on the remote sensing-based extraction of orchard planting years, and the meteorological data-based extraction of the number of fruit tree flowering days and chill accumulation before frost and the minimum temperature and daily temperature difference on the day of frost damage, which, combined with field survey data of orchard yield loss, is used to construct a frost damage loss assessment model.…”

Soil biochemical properties and microbial composition in aged and non‐aged apple (Malus domestica) orchards in Luochuan County, Loess Plateau, China

“…The apple industry has made remarkable advancements in China, but the ageing of orchards affects the quality and yield of apples, reduces the economic output of orchards and hinders the sustainable development of the apple industry. Therefore, the accurate determination of apple planting age is very important for the government to predict regional apple production and assist in the formulation of reform policies for ageing orchards (Zhu et al, 2020). Recently, studies have focused on identifying the age of trees.…”