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Remote Sensing for Forest Characterisation and Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 6505

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Special Issue Editor

Dr. Irfan A. Iqbal

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Guest Editor

School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Australia
Interests: LiDAR; photogrammetry; image analysis; forest modelling; forestry

Special Issue Information

Dear Colleagues,

Over the years, remote sensing has played a vital role in characterising and monitoring forests at various spatial scales. From satellite imagery and aerial photography to the introduction of laser scanning, forest studies have benefited greatly from revolutionary remote sensing datasets and methods. The feasibility of utilising remote sensing datasets for the direct and indirect extraction of various forest parameters has been demonstrated, and the variety of applications continues to increase. With the increased availability of remote sensing data, novel methods continue to develop to characterise our invaluable forest resources at scales ranging from individual trees to the landscape level.

Prospective authors and early researchers are invited to contribute to this Special Issue of Remote Sensing by submitting original manuscripts reporting their latest research results in forest characterisation and monitoring. Reviews contributions are also welcome. Contributions may include, but are not limited to:

New methods in information extraction, i.e., automated feature extraction and object recognition using various forms of remote sensing data;
New developments, e.g., in individual tree-based or area-based inventories;
Methods for upscaling local forest measurements to a landscape and regional level;
Transferability of forest inventory methods;
New applications and concepts using satellite imagery, photogrammetry, and laser scanning for forests;
Techniques for the fusion of ALS and TLS data with that of other sensors;
Integration of ALS and TLS in practical forest measurements;
Mobile terrestrial laser scanning developments;
Accuracy and performance evaluations.

Dr. Irfan A. Iqbal
Guest Editor

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. Remote Sensing 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 2700 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

laser scanning (aerial, terrestrial, and mobile)
data fusion (LiDAR, photogrammetry, and satellite observations)
forest characterisation and monitoring
individual tree detection
area-based inventory
estimation of forest structural variables and upscaling
commercial/strategic forest inventory

Published Papers (4 papers)

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22 pages, 3940 KiB

Open AccessArticle

Examining the Potential of Sentinel Imagery and Ensemble Algorithms for Estimating Aboveground Biomass in a Tropical Dry Forest

by Mike H. Salazar Villegas, Mohammad Qasim, Elmar Csaplovics, Roy González-Martinez, Susana Rodriguez-Buritica, Lisette N. Ramos Abril and Billy Salazar Villegas

Remote Sens. 2023, 15(21), 5086; https://doi.org/10.3390/rs15215086 - 24 Oct 2023

Cited by 1 | Viewed by 1351

Abstract

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models’ field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m². Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R² value of 0.78 and an RMSE value of 42.25 Mg/ha⁻¹. In contrast, the XGBoost model performed less accurately, obtaining an R² value of only 0.60 and an RMSE value of 48.41 Mg/ha⁻¹. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Characterisation and Monitoring)

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25 pages, 10062 KiB

Open AccessArticle

A Novel Approach to Characterizing Crown Vertical Profile Shapes Using Terrestrial Laser Scanning (TLS)

by Fan Wang, Yuman Sun, Weiwei Jia, Dandan Li, Xiaoyong Zhang, Yiren Tang and Haotian Guo

Remote Sens. 2023, 15(13), 3272; https://doi.org/10.3390/rs15133272 - 25 Jun 2023

Cited by 4 | Viewed by 1601

Abstract

Crown vertical profiles (CVP) play an essential role in stand biomass and forest fire prediction. Traditionally, due to measurement difficulties, CVP models developed based on a small number of individual trees are not convincing. Terrestrial laser scanning (TLS) provides new insights for researching trees’ CVPs. However, there is a limited understanding of the ability to accurately describe CVPs with TLS. In this study, we propose a new approach to automatically extract the crown radius (CR) at different heights and confirm the correctness and effectiveness of the proposed approach with field measurement data from 30 destructively harvested sample trees. We then applied the approach to extract the CR from 283 trees in 6 sample plots to develop a two-level nonlinear mixed-effects (NLME) model for the CVP. The results of the study showed that the average extraction accuracy of the CR when the proposed approach was applied was 90.12%, with differences in the extraction accuracies at different relative depths into the crown (RDINC) ranges. The TLS-based extracted CR strongly correlated with the field-measured CR, with an R² of 0.93. Compared with the base model, the two-level NLME model has significantly improved the prediction accuracy, with R_a² increasing by 13.8% and RMSE decreasing by 23.46%. All our research has demonstrated that TLS has great potential for accurately extracting CRs, which would provide a novel way to nondestructively measure the crown structure. Moreover, our research lays the foundation for the future development of CVP models using TLS at a regional scale. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Characterisation and Monitoring)

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26 pages, 10908 KiB

Open AccessArticle

Forest Area and Structural Variable Estimation in Boreal Forest Using Suomi NPP VIIRS Data and a Sample from VHR Imagery

by Tuomas Häme, Heikki Astola, Jorma Kilpi, Yrjö Rauste, Laura Sirro, Teemu Mutanen, Eija Parmes, Jussi Rasinmäki and Mohammad Imangholiloo

Remote Sens. 2023, 15(12), 3029; https://doi.org/10.3390/rs15123029 - 9 Jun 2023

Viewed by 1424

Abstract

Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Characterisation and Monitoring)

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15 pages, 7600 KiB

Open AccessTechnical Note

Crown Information Extraction and Annual Growth Estimation of a Chinese Fir Plantation Based on Unmanned Aerial Vehicle–Light Detection and Ranging

by Jingfeng Xiong, Hongda Zeng, Guo Cai, Yunfei Li, Jing M. Chen and Guofang Miao

Remote Sens. 2023, 15(15), 3869; https://doi.org/10.3390/rs15153869 - 4 Aug 2023

Cited by 2 | Viewed by 1191

Abstract

Forest biomass dynamics are important indicators of forest productivity and carbon sinks, which are useful for evaluating forest ecological benefits and management options. Rapid and accurate methods for monitoring forest biomass would serve this purpose well. This study aimed at measuring aboveground biomass (AGB) and stand growth from tree crown parameters derived using unmanned aerial vehicle–light detection and ranging (UAV–LiDAR). We focused on 17-year-old Chinese fir plantations in a subtropical area in China and monitored them using UAV–LiDAR from February 2019 to February 2020. Two effective crown height (ECH) detection methods based on drone discrete point clouds were evaluated using ground survey data. Based on the evaluation results, the voxel method based on point cloud segmentation (root-mean-squared error (RMSE) = 0.62 m, relative RMSE (rRMSE) = 4.26%) was better than the tree crown boundary pixel sum method based on canopy height segmentation (RMSE = 1.26 m, rRMSE = 8.63%). The effective crown area (ECA) of an individual tree extracted using ECH was strongly correlated with the annual biomass growth (coefficient of determination (R²) = 0.47). The estimation of annual growth of individual tree crowns based on annual tree height increase (ΔH) derived from LiDAR was statistically significant (R² = 0.33, p < 0.01). After adding the crown projection area or ECA, the model accuracy R² increased to 0.57 or 0.63, respectively. As the scale increased to the plot level, the direct model with ECA (RMSE = 1.59 Mg∙ha⁻¹∙a⁻¹, rRMSE = 15.02%) had a better performance than the indirect model using tree height and crown diameter (RMSE = 1.81 Mg∙ha⁻¹∙a⁻¹, rRMSE = 17.10%). The mean annual growth rate of AGB per middle-aged Chinese fir tree was determined to be 8.45 kg∙a⁻¹ using ECA and ΔH, and the plot-level growth rate was 11.47 Mg∙ha⁻¹∙a⁻¹. We conclude that the rapid and accurate monitoring of the annual growth of Chinese fir can be achieved based on multitemporal UAV–LiDAR and effective crown information. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Characterisation and Monitoring)

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