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Estimating and Monitoring Forest Structure Using Remote Sensing Techniques

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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 May 2022) | Viewed by 38425

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Dr. Paola Rizzoli

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Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, Germany
Interests: synthetic aperture radar; SAR interferometry; digital elevation models; SAR system design; forest mapping and monitoring; image processing; artificial intelligence
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Dr. Armando Marino

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Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
Interests: remote sensing; synthetic aperture radar (SAR); polarimetric SAR; forest aboveground biomass; polarimetric target detector
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Special Issue Information

Dear Colleagues,

As a vital natural resource, forests are of extreme importance for all living beings on our planet. We would like to dedicate this Special Issue to documenting remote sensing-based methods for forest structure retrieval, forest degradation monitoring, and forest resources assessment. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:

New methods for the retrieval of forest structure parameters from remote sensing data, including SAR and lidar

Combination of complementary SAR imaging methods (tomography, polarimetry, interferometry), lidar sensors as well as data fusion with optical to define novel approaches, concepts, and applications for forest structure mapping and monitoring

New methods and concepts for the quantitative assessment of forest biomass

Feasibility studies with new sensors, ranging from drones to spaceborne SAR systems and their applications to forestry

Comparison and benchmarking studies using various sensors and/or processing methods for forest structure retrieval

New approaches for the detection of forest changes and degradation

Artificial intelligence-based methods and multi-sensor data fusion for forest information retrieval

Dr. Paola Rizzoli
Dr. Armando Marino
Guest Editors

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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

forest structure
remote sensing
biomass
artificial intelligence
data fusion

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Published Papers (10 papers)

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Research

26 pages, 4708 KiB

Open AccessArticle

Performance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia

by Sonam Dhargay, Christopher S. Lyell, Tegan P. Brown, Assaf Inbar, Gary J. Sheridan and Patrick N. J. Lane

Remote Sens. 2022, 14(15), 3615; https://doi.org/10.3390/rs14153615 - 28 Jul 2022

Cited by 27 | Viewed by 4241

Abstract

Monitoring forest structural properties is critical for a range of applications because structure is key to understanding and quantifying forest biophysical functioning, including stand dynamics, evapotranspiration, habitat, and recovery from disturbances. Monitoring of forest structural properties at desirable frequencies and cost globally is enabled by space-borne LiDAR missions such as the global ecosystem dynamics investigation (GEDI) mission. This study assessed the accuracy of GEDI estimates for canopy height, total plant area index (PAI), and vertical profile of plant area volume density (PAVD) and elevation over a gradient of canopy height and terrain slope, compared to estimates derived from airborne laser scanning (ALS) across two forest age-classes in the Central Highlands region of south-eastern Australia. ALS was used as a reference dataset for validation of GEDI (Version 2) dataset. Canopy height and total PAI analyses were carried out at the landscape level to understand the influence of beam-type, height of the canopy, and terrain slope. An assessment of GEDI’s terrain elevation accuracy was also carried out at the landscape level. The PAVD profile evaluation was carried out using footprints grouped into two forest age-classes, based on the areas of mountain ash (Eucalyptus regnans) forest burnt in the Central Highlands during the 1939 and 2009 wildfires. The results indicate that although GEDI is found to significantly under-estimate the total PAI and slightly over-estimate the canopy height, the GEDI estimates of canopy height and the vertical PAVD profile (above 25 m) show a good level of accuracy. Both beam-types had comparable accuracies, with increasing slope having a slightly detrimental effect on accuracy. The elevation accuracy of GEDI found the RMSE to be 10.58 m and bias to be 1.28 m, with an R² of 1.00. The results showed GEDI is suitable for canopy densities and height in complex forests of south-eastern Australia. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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23 pages, 5812 KiB

Open AccessArticle

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

by Kaile Yang, Houxi Zhang, Fan Wang and Riwen Lai

Remote Sens. 2022, 14(10), 2469; https://doi.org/10.3390/rs14102469 - 20 May 2022

Cited by 13 | Viewed by 2637

Abstract

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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17 pages, 24055 KiB

Open AccessArticle

Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

by Kunyong Yu, Zhenbang Hao, Christopher J. Post, Elena A. Mikhailova, Lili Lin, Gejin Zhao, Shangfeng Tian and Jian Liu

Remote Sens. 2022, 14(2), 295; https://doi.org/10.3390/rs14020295 - 11 Jan 2022

Cited by 53 | Viewed by 6389

Abstract

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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19 pages, 11524 KiB

Open AccessArticle

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

by Guilin Xi, Xiaojun Huang, Yaowen Xie, Bao Gang, Yuhai Bao, Ganbat Dashzebeg, Tsagaantsooj Nanzad, Altanchimeg Dorjsuren, Davaadorj Enkhnasan and Mungunkhuyag Ariunaa

Remote Sens. 2022, 14(1), 124; https://doi.org/10.3390/rs14010124 - 28 Dec 2021

Cited by 13 | Viewed by 2669

Abstract

Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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28 pages, 10109 KiB

Open AccessArticle

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

by Chunhua Qian, Hequn Qiang, Feng Wang and Mingyang Li

Remote Sens. 2021, 13(24), 5030; https://doi.org/10.3390/rs13245030 - 10 Dec 2021

Cited by 11 | Viewed by 3268

Abstract

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R² of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R² of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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17 pages, 7329 KiB

Open AccessArticle

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

by Kateřina Gdulová, Jana Marešová, Vojtěch Barták, Marta Szostak, Jaroslav Červenka and Vítězslav Moudrý

Remote Sens. 2021, 13(15), 3042; https://doi.org/10.3390/rs13153042 - 3 Aug 2021

Cited by 12 | Viewed by 2587

Abstract

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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25 pages, 6693 KiB

Open AccessArticle

Mapping Tree Height in Burkina Faso Parklands with TanDEM-X

by Maciej J. Soja, Martin Karlson, Jules Bayala, Hugues R. Bazié, Josias Sanou, Boalidioa Tankoano, Leif E. B. Eriksson, Heather Reese, Madelene Ostwald and Lars M. H. Ulander

Remote Sens. 2021, 13(14), 2747; https://doi.org/10.3390/rs13142747 - 13 Jul 2021

Cited by 2 | Viewed by 2780

Abstract

Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Saponé, Burkina Faso, a test site characterised by a low average canopy cover (~15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January–April 2018 are used jointly to create high-resolution (~3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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24 pages, 5729 KiB

Open AccessEditor’s ChoiceArticle

A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests

by Heather Grybas and Russell G. Congalton

Remote Sens. 2021, 13(13), 2631; https://doi.org/10.3390/rs13132631 - 4 Jul 2021

Cited by 29 | Viewed by 4417

Abstract

Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a sequence of mono-temporal and multi-temporal classifications were carried out. Kappa comparisons were conducted to ascertain whether the multi-temporal classifications significantly improved accuracy and whether there were significant differences between the RGB and multispectral classifications. The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used. Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time. The RGB sensor exhibited significantly higher accuracies, probably due to the blue band, which was found to be very important for classification accuracy and lacking in the multispectral sensor employed here. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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21 pages, 6497 KiB

Open AccessArticle

Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR

by Ben Spracklen and Dominick V. Spracklen

Remote Sens. 2021, 13(7), 1233; https://doi.org/10.3390/rs13071233 - 24 Mar 2021

Cited by 12 | Viewed by 4502

Abstract

A forest’s structure changes as it progresses through developmental stages from establishment to old-growth forest. Therefore, the vertical structure of old-growth forests will differ from that of younger, managed forests. Free, publicly available spaceborne Laser Range and Detection (LiDAR) data designed for the determination of forest structure has recently become available through NASA’s General Ecosystem and Development Investigation (GEDI). We use this data to investigate the structure of some of the largest remaining old-growth forests in Europe in the Ukrainian Carpathian Mountains. We downloaded 18489 cloud-free shots in the old-growth forest (OGF) and 20398 shots in adjacent non-OGF areas during leaf-on, snow-free conditions. We found significant differences between OGF and non-OGF over a wide range of structural metrics. OGF was significantly more open, with a more complex vertical structure and thicker ground-layer vegetation. We used Random Forest classification on a range of GEDI-derived metrics to classify OGF shapefiles with an accuracy of 73%. Our work demonstrates the use of spaceborne LiDAR for the identification of old-growth forests. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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20 pages, 88964 KiB

Open AccessArticle

A Novel Four-Stage Method for Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal Decorrelation Adaptive Estimation and Distance Transformation

by Cheng Xing, Tao Zhang, Hongmiao Wang, Liang Zeng, Junjun Yin and Jian Yang

Remote Sens. 2021, 13(2), 213; https://doi.org/10.3390/rs13020213 - 9 Jan 2021

Cited by 9 | Viewed by 2746

Abstract

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m). Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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