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Advances in Remote Sensing for 3D Plant Modelling

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Special Issue Editors

Prof. Dr. Francisco Ramón Feito Higueruela

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Guest Editor

Department of Computer Sciences, University of Jaén, Jaen, Spain
Interests: computer graphics; geomatics; geographical information systems

Prof. Dr. Juan Manuel Jurado Rodríguez

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Guest Editor

Department of Computer Sciences, University of Jaén, Jaen, Spain
Interests: computer graphics; computer vision; multispectral and hyperspectral imaging; 3D modeling; multisource data fusion

Special Issue Information

Dear Colleagues,

3D modeling of plants is a trending research topic which still presents several open problems. The proliferation of novel acquisition systems based on UAVs, terrestrial laser scanners, and LiDAR technology enables the possibility to get detailed knowledge of the 3D structure of vegetation from real-world environments. In this Special Issue, we are interested in novel methodologies focused on plant modeling using remote sensing techniques. In this regard, there are several promising research lines pushing in this direction related to procedural modeling, inverse modeling, and guided procedural modeling which use real-word data as a reference to model the 3D plant structure and plant foliage as realistically as possible. Likewise, such methods focused on realistic simulations of 3D plant models under environmental effects are highly demanded in this topic. Finally, the study of plant geometry usually represents complex geometric shapes whose semantic segmentation is another challenging task to address. This Special Issue on “Advances in Remote Sensing for 3D Plant Modeling” calls for studies that present innovative and/or disruptive ideas, and investigation results that integrate remote sensing data to advance 3D plant reconstruction.

Prof. Dr. Francisco Ramón Feito Higueruela
Prof. Dr. Juan Manuel Jurado Rodríguez
Guest Editors

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Research

16 pages, 6085 KiB

Open AccessArticle

Forest 3D Reconstruction and Individual Tree Parameter Extraction Combining Close-Range Photo Enhancement and Feature Matching

by Ruoning Zhu, Zhengqi Guo and Xiaoli Zhang

Remote Sens. 2021, 13(9), 1633; https://doi.org/10.3390/rs13091633 - 22 Apr 2021

Cited by 22 | Viewed by 4305

Abstract

An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for 3D Plant Modelling)

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