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Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 52148

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Dr. Marcos Barrio-Anta

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Guest Editor

Department of Organisms and Systems Biology, GIS-Forest Research Group, Polytechnic School of Mieres, University of Oviedo, Asturias, Spain
Interests: biodiversity & conservation; forest ecology; forest management; silviculture

Dr. Carlos A. López-Sánchez

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Guest Editor

Smart Forest Group, Department of Biology of Organisms and Systems, Mieres Polytechnic School, University of Oviedo, 33600 Oviedo, Spain
Interests: remote sensing; forest management and modelling; biogeography and conservation; global change
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Special Issue Information

Dear Colleagues,

The structure, species composition (occurrence and abundance) and productivity of forests and how they are explicit in time and space are key pieces of information for forest management.

Recent advances in remote sensing technologies allow us to capture large datasets on species-specific tree and stand attributes from multiple measurement systems. The new ways to analyze and process these datasets (e.g., novel machine learning algorithms) provide new insights necessary to generate spatially explicit information. This information has great value for nature conservationists as well as for forest managers that frequently require it to be displayed for large spatial extents.

In this Special Issue, the guest editors encourage the submission of current research that use data acquired with a variety of remote sensing technologies (airborne and terrestrial laser scanning (ALS/TLS), digital aerial photogrammetry (DAP), and high/very high spatial resolution (HSR/VHSR) satellite optical imagery) under different inventory approaches—the area-based approach (ABA) and the individual tree detection (ITD) approach—designed to characterize forest resource information for strategic, tactical, and operational planning. We would particularly welcome submissions on multi-sensor data fusion.

Dr. Marcos Barrio-Anta
Dr. Carlos A. Lopez-Sanchez
Guest Editors

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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. Forests is an international peer-reviewed open access monthly 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 2600 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 Inventory
Remote Sensing
Multispectral and Hyperspectral Imagery
Airborne and Terrestrial Laser Scanning
Tree Species Composition
Land Use and Land Cover (LULC)
Structural Diversity
Vertical Canopy Distributions
Area-Based Approach (ABA)
Individual Tree Detection (ITD)

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Published Papers (11 papers)

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Research

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14 pages, 3131 KiB

Open AccessArticle

Automatic Delineation of Forest Patches in Highly Fragmented Landscapes Using Coloured Point Clouds

by José V. Roces-Díaz, Carlos Cabo, Covadonga Prendes, Celestino Ordoñez and Cristina Santín

Forests 2020, 11(2), 198; https://doi.org/10.3390/f11020198 - 11 Feb 2020

Cited by 5 | Viewed by 3171

Abstract

Accurate mapping of landscape features is key for natural resources management and planning. For this purpose, the use of high-resolution remote sensing data has become widespread and is increasingly freely available. However, mapping some target features, such as small forest patches, is still a challenge. Standard, easily replicable, and automatic methodologies to delineate such features are still missing. A common alternative to automated methods is manual delineation, but this is often too time and resource intensive. We developed a simple and automatic method from freely available aerial light detection and ranging (LiDAR) and aerial ortho-images that provide accurate land use mapping and overcome some of the aforementioned limitations. The input for the algorithm is a coloured point cloud, where multispectral information from the ortho-images is associated to each LiDAR point. From this, four-class segmentation and mapping were performed based on vegetation indices and the ground-elevation of the points. We tested the method in four areas in the north-western Iberian Peninsula and compared the results with existent cartography. The completeness and correctness of our algorithm ranging between 78% and 99% in most cases, and it allows for the delineation of very small patches that were previously underrepresented in the reference cartography. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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25 pages, 5006 KiB

Open AccessArticle

Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

by Yanshuang Wu and Xiaoli Zhang

Forests 2020, 11(1), 32; https://doi.org/10.3390/f11010032 - 24 Dec 2019

Cited by 40 | Viewed by 4767

Abstract

The identification of tree species is one of the most basic and key indicators in forest resource monitoring with great significance in the actual forest resource survey and it can comprehensively improve the efficiency of forest resource monitoring. The related research has mainly focused on single tree species without considering multiple tree species, and therefore the ability to classify forest tree species in complex stand is not clear, especially in the subtropical monsoon climate region of southern China. This study combined airborne hyperspectral data with simultaneously acquired LiDAR data, to evaluate the capability of feature combinations and k-nearest neighbor (KNN) and support vector machine (SVM) classifiers to identify tree species, in southern China. First, the stratified classification method was used to remove non-forest land. Second, the feature variables were extracted from airborne hyperspectral image and LiDAR data, including independent component analysis (ICA) transformation images, spectral indices, texture features, and canopy height model (CHM). Third, random forest and recursion feature elimination methods were adopted for feature selection. Finally, we selected different feature combinations and used KNN and SVM classifiers to classify tree species. The results showed that the SVM classifier has a higher classification accuracy as compared with KNN classifier, with the highest classification accuracy of 94.68% and a Kappa coefficient of 0.937. Through feature elimination, the classification accuracy and performance of SVM classifier was further improved. Recursive feature elimination method based on SVM is better than random forest. In the spectral indices, the new constructed slope spectral index, SL₂, has a certain effect on improving the classification accuracy of tree species. Texture features and CHM height information can effectively distinguish tree species with similar spectral features. The height information plays an important role in improving the classification accuracy of other broad-leaved species. In general, the combination of different features can improve the classification accuracy, and the proposed strategies and methods are effective for the identification of tree species at complex forest type in southern China. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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18 pages, 10867 KiB

Open AccessArticle

Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

by Haijian Liu and Changshan Wu

Forests 2020, 11(1), 28; https://doi.org/10.3390/f11010028 - 23 Dec 2019

Cited by 18 | Viewed by 4407

Abstract

LiDAR (Light Detection and Ranging)-based individual tree crown reconstruction is a challenge task due to the variable canopy morphologies and the penetrating properties of LiDAR to tree crown surfaces. Traditional methods, including LiDAR-derived rasterization, low-pass filtering smooth algorithm, and original triangular irregular network (TIN) model, have difficulties in balancing morphological accuracy and model smoothness. To address this issue, a scene-based TIN was generated with three steps based on the local scene principle. First, local Delaunay triangles were formed through connecting neighboring point sets. Second, key control points within each local Delaunay triangle, including steeple, inverted tip, ridge, saddle, and horseshoe shape control points, were extracted by analyzing multiple local scenes. These key points were derived to determine the fluctuations of forest canopies. Third, the scene-based TIN model was generated using the control points as nodes. Visual analysis indicates the new model can accurately reconstruct different canopy shapes with a relatively smooth surface, and statistical analysis of individual trees confirms that the overall error of the new model is smaller than others. Especially, the scene-based TIN derived raster reduced the average error to 0.18 m, with a standard deviation of 0.41, while the average errors of LiDAR-derived raster, low-pass filtered smooth raster, and original TIN derived raster have average errors of 0.96, 2.05, and 1.00 m, respectively. The local scene-based control point extraction also reduces data storage due to the elimination of redundant points, and furthermore the different point densities on different objects are beneficial for canopy segmentation. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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16 pages, 5860 KiB

Open AccessArticle

Sensitivity of Vegetation on Alpine and Subalpine Timberline in Qinling Mountains to Temperature Change

by Xinping Ma, Hongying Bai, Chenhui Deng and Tao Wu

Forests 2019, 10(12), 1105; https://doi.org/10.3390/f10121105 - 3 Dec 2019

Cited by 21 | Viewed by 3100

Abstract

Alpine timberline is a great place for monitoring climate change. The study of alpine and subalpine timberline in Qinling Mountains has led to early warning that reveals the response and adaptation of terrestrial vegetation ecosystem to climate change. Based on the remote sensing image classification method, the typical timberline area in Qinling Mountains was determined. Temperature and normalized difference vegetation index (NDVI) data were extracted from the typical timberline area based on spatial interpolation and NDVI data. The relationship between NDVI and temperature change and the critical temperature value affecting vegetation response in the timberline area in Qinling Mountains were analyzed. Correlation between NDVI and air temperature in the alpine and subalpine timberline areas of Qinling Mountains exhibited an upward trend, which implied that temperature promotes vegetation activity. A strong correlation between temperature and NDVI in typical timberline areas of Qinling Mountains, and a significant correlation between temperature and NDVI in the early growing season. A phenomenon of NDVI lagging behind air temperature was observed. Temperature response showed synchronization and hysteresis. The correlation between cumulative temperature and vegetation was similar between Taibai Mountain and Niubeiliang timberline, and the correlation between NDVI in April and cumulative temperature in the first 12 months was the strongest. Temperature threshold range of Taibai Mountain timberline played a dominant role in vegetation growth. Our results provide insights and basis for future studies of early warning signs of climate change, specifically between 0.34 and 1.34 °C. The threshold ranges of temperature response of different vegetation types vary. Compared with alpine shrub meadow, the threshold ranges of temperature effect of Coniferous forest and Larix chinensis Beissn. are smaller, implying that these vegetation types are more sensitive to temperature change. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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15 pages, 3630 KiB

Open AccessArticle

Tree Species Classification by Integrating Satellite Imagery and Topographic Variables Using Maximum Entropy Method in a Mongolian Forest

by Shou-Hao Chiang and Miguel Valdez

Forests 2019, 10(11), 961; https://doi.org/10.3390/f10110961 - 1 Nov 2019

Cited by 23 | Viewed by 5168

Abstract

Forests are an important natural resource that achieve ecological balance by regulating water regimes and promoting soil conservation. Based on forest inventories, the government is able to make decisions to sustainably conserve, improve, and manage forests. Fieldwork for forestry investigation requires intensive physical labor, which is costly and time-consuming, especially for surveys in remote mountainous regions. Remote sensing technology has been recently used for forest investigation on a large scale. An informative forest inventory must include forest attributes, including details of tree species; however, tree species mapping is not always applicable due to the similarity of surface reflectance and texture between tree species. Topographic variables such as elevation, slope, aspect, and curvature are crucial in allocating ecological niches to different species; therefore, this study suggests that integrating topographic information and optical satellite image classification can improve mapping accuracy for tree species. The main purpose of this study is to classify forest tree species in Erdenebulgan County, Huwsgul Province, Mongolia, by integrating Landsat satellite imagery with a Digital Elevation Model (DEM) using a Maximum Entropy algorithm. A forest tree species inventory from the Forest Division of the Mongolian Ministry of Nature and Environment was used as training data and as ground truth to perform the accuracy assessment. In this study, the classification was made using two different experimental approaches. First, classification was done using only Landsat surface reflectance data; and second, topographic variables were integrated with the Landsat surface reflectance data. The integration approach showed a higher overall accuracy and kappa coefficient, indicating that an accurate forest inventory can be achieved by integrating satellite imagery data and other topographic information to enhance the practice of forest management in remote regions. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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17 pages, 5012 KiB

Open AccessArticle

Extraction of Information on Trees outside Forests Based on Very High Spatial Resolution Remote Sensing Images

by Bin Sun, Zhihai Gao, Longcai Zhao, Hongyan Wang, Wentao Gao and Yuanyuan Zhang

Forests 2019, 10(10), 835; https://doi.org/10.3390/f10100835 - 23 Sep 2019

Cited by 9 | Viewed by 3364

Abstract

The sparse Ulmus pumila L. woodland in the Otingdag Sandy Land of China is indispensable in maintaining the ecosystem stability of the desertified grasslands. Many studies of this region have focused on community structure and analysis of species composition, but without consideration of spatial distribution. Based on a combination of spectral and multiscale spatial variation features, we present a method for automated extraction of information on the U. pumila trees of the Otingdag Sandy Land using very high spatial resolution remote sensing imagery. In this method, feature images were constructed using fused 1-m spatial resolution GF-2 images through analysis of the characteristics of the natural geographical environment and the spatial distribution of the U. pumila trees. Then, a multiscale Laplace transform was performed on the feature images to generate multiscale Laplacian feature spaces. Next, local maxima and minima were obtained by iteration over the multiscale feature spaces. Finally, repeated values were removed and vector data (point data) were generated for automatic extraction of the spatial distribution and crown contours of the U. pumila trees. Results showed that the proposed method could overcome the lack of universality common to image classification methods. Validation indicated the accuracy of information extracted from U. pumila test data reached 82.7%. Further analysis determined the parameter values of the algorithm applicable to the study area. Extraction accuracy was improved considerably with a gradual increase of the Sigma parameter; however, the probability of missing data also increased markedly after the parameter reached a certain level. Therefore, we recommend the Sigma value of the algorithm be set to 90 (±5). The proposed method could provide a reference for information extraction, spatial distribution mapping, and forest protection in relation to the U. pumila woodland of the Otingdag Sandy Land, which could also support improved ecological protection across much of northern China. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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19 pages, 18290 KiB

Open AccessArticle

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

by Xisheng Zhou, Long Li, Longqian Chen, Yunqiang Liu, Yifan Cui, Yu Zhang and Ting Zhang

Forests 2019, 10(6), 478; https://doi.org/10.3390/f10060478 - 31 May 2019

Cited by 21 | Viewed by 4527

Abstract

Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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17 pages, 1753 KiB

Open AccessArticle

Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

by Alejandra del Pilar Ochoa-Franco, José René Valdez-Lazalde, Gregorio Ángeles-Pérez, Hector Manuel de los Santos-Posadas, José Luis Hernández-Stefanoni, Juan Ignacio Valdez-Hernández and Paulino Pérez-Rodríguez

Forests 2019, 10(5), 419; https://doi.org/10.3390/f10050419 - 15 May 2019

Cited by 7 | Viewed by 4064

Abstract

Tree beta-diversity denotes the variation in species composition at stand level, it is a key indicator of forest degradation, and is conjointly required with alpha-diversity for management decision making but has seldom been considered. Our aim was to map it in a continuous way with remote sensing technologies over a tropical landscape with different disturbance histories. We extracted a floristic gradient of dissimilarity through a non-metric multidimensional scaling ordination based on the ecological importance value of each species, which showed sensitivity to different land use history through significant differences in the gradient scores between the disturbances. After finding strong correlations between the floristic gradient and the rapidEye multispectral textures and LiDAR-derived variables, it was linearly regressed against them; variable selection was performed by fitting mixed-effect models. The redEdge band mean, the Canopy Height Model, and the infrared band variance explained 68% of its spatial variability, each coefficient with a relative importance of 49%, 32.5%, and 18.5% respectively. Our results confirmed the synergic use of LiDAR and multispectral sensors to map tree beta-diversity at stand level. This approach can be used, combined with ground data, to detect effects (either negative or positive) of management practices or natural disturbances on tree species composition. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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21 pages, 3148 KiB

Open AccessArticle

Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

by Jian Li, Jingwen He, Ying Liu, Daojie Wang, Loretta Rafay, Can Chen, Tao Hong, Hailan Fan and Yongming Lin

Forests 2019, 10(2), 195; https://doi.org/10.3390/f10020195 - 21 Feb 2019

Cited by 16 | Viewed by 4205

Abstract

Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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16 pages, 7801 KiB

Open AccessArticle

The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

by Yingying Yang, Taixia Wu, Shudong Wang, Jing Li and Farhan Muhanmmad

Forests 2019, 10(2), 139; https://doi.org/10.3390/f10020139 - 8 Feb 2019

Cited by 24 | Viewed by 7195

Abstract

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVI_ann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVI_ann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVI_ann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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Review

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18 pages, 3199 KiB

Open AccessReview

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

by Pablito M. López-Serrano, José Luis Cárdenas Domínguez, José Javier Corral-Rivas, Enrique Jiménez, Carlos A. López-Sánchez and Daniel José Vega-Nieva

Forests 2020, 11(1), 11; https://doi.org/10.3390/f11010011 - 19 Dec 2019

Cited by 72 | Viewed by 7458

Abstract

An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha⁻¹). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit. Full article

(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)

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