Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Xinyu Dong,Zhichao Zhang,Ruiyang Yu,Xicun Zhu,Qingjiu Tian

doi:10.3390/rs12010133

Abstract

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

Highlights

Precision Farming refers to the observation of crop growth and timely strategic responses to small variations in crop production [1]
The remote sensing methods that were constructed based on the above data have advantages over traditional manual surveys, such as a higher spatiotemporal resolution
Though the spatial resolution of the remote sensing data from a satellite platform could be in the decimeter range (e.g., WorldView-4), it is difficult to use these data to monitor the details of individual tree-crowns

Summary

Introduction

Precision Farming (or Precision Agriculture) refers to the observation of crop growth and timely strategic responses to small variations in crop production [1]. As a technology-enabled, information-based, and decision-focused system, precision farming has been successfully applied in field crop production and horticulture, including orchards [3]. Aerial remote sensing data may provide resolutions at the centimeter level that could precisely observe the individual fruit trees in an orchard. Remote sensing image data from a satellite platform have been extensively applied on a large scale due to their wide coverage and the abundance of spectral information that they provide, such as crop identification [4], fruit crop plantation mapping [5], crops acreage estimation [6], and individual tree detection [7]. Satellite remote sensing image data are relatively difficult to use to monitor horticultural crop structure parameters at the level of individual trees

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