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Applied Sciences
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Spatial Information Technology in Forest Ecosystem
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Spatial Information Technology in Forest Ecosystem

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4091

Special Issue Editors

Dr. Yuanyong Dian

E-Mail Website
Guest Editor
College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, China
Interests: forest measurements; forest management; remote sensing; modelling; LiDAR
Dr. Jingjing Zhou

E-Mail Website
Guest Editor
College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, China
Interests: silviculture; foest sturcutre and function; remote sensing; hypersecptral analysis

Special Issue Information

Dear Colleagues,

The forest is the most important terrestrial ecosystem and the structure and function of the forest have been widely reported in research. With the development of spatial information technologies, remote sensing (RS), geographic information systems (GIS), UAV, LiDAR, and hyperspectral technology are widely used in forest ecosystem research. This Special Issue aims to disseminate the latest research on the application of spatial information technology in forest ecology, which will help humans to solve typical problems in forest ecosystem management. The topics include but are not limited to the following:

(a) Forest ecosystem assessment with GIS or RS;

(b) Forest ecosystem parameters retrieval with RS;

(c) Forest diversity mapping;

(d) Forest structure or function detection with spatial technologies;

(e) Spatial monitoring and predictive analysis of forest hazards such as pests or diseases;

(f) Application of UAV, LiDAR, or hyperspectral remote sensing in the forest.

Dr. Yuanyong Dian
Dr. Jingjing Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • forest measurements
  • forest management
  • remote sensing
  • modelling
  • LiDAR

Published Papers (3 papers)

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Research

10 pages, 1619 KiB  
Communication
Presence of Quercus Suber Soft-Leaf Defoliators on Trees with Distinct Foliar Monoterpene Emission Profiles
by Israel Sánchez-Osorio, Daniel Robles and Raúl Tapias
Appl. Sci. 2024, 14(3), 1112; https://doi.org/10.3390/app14031112 - 29 Jan 2024
Viewed by 522
Abstract
The cork tree, Quercus suber L., is a characteristic species of the Dehesa agrosilvopastoral system, typical of western Spain. Defoliating insects are an important component of these ecosystems. This study assessed the presence and impact of defoliators feeding on Q. suber soft leaf [...] Read more.
The cork tree, Quercus suber L., is a characteristic species of the Dehesa agrosilvopastoral system, typical of western Spain. Defoliating insects are an important component of these ecosystems. This study assessed the presence and impact of defoliators feeding on Q. suber soft leaf tissue and their relationship with foliar monoterpene emission profiles. Samplings consisted of weekly tree beating (15 times per tree per sample) during the flight period of key species. We studied 26 cork trees with known profiles of foliar monoterpene emission (13 pinene and 13 limonene chemotypes). We identified a total of 272 larvae from 9 species. The main species were Catocala nymphagoga Esper (40.8%) and Periclista andrei Know (27.6%). Notably, 70.6% of larvae were found on trees with a pinene chemotype. The combined abundance of the four key defoliator species (C. nymphagoga L., P. andrei, Bena bicolorana L., and Cyclophora punctaria L.) was 62.7% lower on limonene-chemotype trees than pinene-chemotype trees. Significant differences were found in defoliation damage between leaves with distinct terpene emission profiles. These results suggest that both the abundance and damage caused by defoliators differ with trees’ emission profiles, and this may indicate differences in palatability and/or nutritional quality between Q. suber trees with distinct foliar monoterpene emission profiles. Full article
(This article belongs to the Special Issue Spatial Information Technology in Forest Ecosystem)
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18 pages, 4335 KiB  
Article
Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
by Zhu Yang, Xuanrui Huang, Yunxian Qing, Hongqian Li, Libin Hong and Wei Lu
Appl. Sci. 2023, 13(15), 8777; https://doi.org/10.3390/app13158777 - 29 Jul 2023
Viewed by 962
Abstract
The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In [...] Read more.
The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI (R2 = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas. Full article
(This article belongs to the Special Issue Spatial Information Technology in Forest Ecosystem)
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20 pages, 5556 KiB  
Article
Identification of Tree Species in Forest Communities at Different Altitudes Based on Multi-Source Aerial Remote Sensing Data
by Haoran Lin, Xiaoyang Liu, Zemin Han, Hongxia Cui and Yuanyong Dian
Appl. Sci. 2023, 13(8), 4911; https://doi.org/10.3390/app13084911 - 13 Apr 2023
Cited by 4 | Viewed by 2080
Abstract
The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this [...] Read more.
The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification. Full article
(This article belongs to the Special Issue Spatial Information Technology in Forest Ecosystem)
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