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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 (15 March 2019) | Viewed by 89325

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Special Issue Editor

Dr. Temuulen Sankey

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School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S. Knoles Ave, Flagstaff, AZ 86011, USA
Interests: UAV; Lidar; SfM; hyperspectral images; data fusion; hyperspectral lidar data

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicle (UAV) applications are rapidly expanding and revolutionizing remote sensing for natural sciences. UAV platforms provide a unique opportunity for acquiring low-cost imagery at fine spatial and temporal resolutions from local to regional scales. Recent advances in UAV sensors include LiDAR and hyperspectral sensors, although multispectral and thermal sensors have been commonly used on UAV platforms. UAV-based visible (RGB) and near-infrared (NIR) images are also used to generate three-dimensional models of topography and vegetation using structure from motion (SfM). Taken together, the UAV sensors, associated images, and derived products can now provide critical datasets for forest monitoring, impact assessment, change detection, and management protocols. This special issue examines UAV-based multispectral, hyperspectral, and three-dimensional images in forestry applications. In particular, this Special Issue focuses on quantitative assessment of errors and accuracies of UAV image-derived forest metrics and variables at the scale of forest stands as well as individual trees. Research papers that focus on both forest metrics and methodological development are welcome. This Special Issue includes the following potential topics, but is not limited to them:

Accuracies and errors in UAV image-derived forest stand metrics (forest yield, biomass, tree density, stand age, etc)
Accuracies and errors associated with UAV image-derived individual tree metrics (tree height, canopy dimension, volume, biomass, etc)
Detection capabilities of UAV multispectral data (individual species classification and characterization)
Detection capabilities of UAV LiDAR data (individual tree detection, tree- and stand-level estimates of biomass, growth, and changes)
Detection capabilities of UAV-derived SfM data (individual tree- and stand-level metrics)

Dr. Temuulen Sankey
Guest Editor

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Keywords

Forest structure
Tree species classification
Forest cover change
UAV lidar
Structure-from-Motion
UAV multispectral data
Data fusion

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Published Papers (10 papers)

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Research

12 pages, 2599 KiB

Open AccessArticle

Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR

by Bruna Paolinelli Reis, Sebastião Venâncio Martins, Elpídio Inácio Fernandes Filho, Tathiane Santi Sarcinelli, José Marinaldo Gleriani, Gustavo Eduardo Marcatti, Helio Garcia Leite and Melinda Halassy

Remote Sens. 2019, 11(13), 1508; https://doi.org/10.3390/rs11131508 - 26 Jun 2019

Cited by 10 | Viewed by 6601

Abstract

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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24 pages, 1672 KiB

Open AccessArticle

Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

by Camile Sothe, Michele Dalponte, Cláudia Maria de Almeida, Marcos Benedito Schimalski, Carla Luciane Lima, Veraldo Liesenberg, Gabriela Takahashi Miyoshi and Antonio Maria Garcia Tommaselli

Remote Sens. 2019, 11(11), 1338; https://doi.org/10.3390/rs11111338 - 3 Jun 2019

Cited by 108 | Viewed by 8603

Abstract

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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22 pages, 7074 KiB

Open AccessArticle

Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

by Giandomenico De Luca, João M. N. Silva, Sofia Cerasoli, João Araújo, José Campos, Salvatore Di Fazio and Giuseppe Modica

Remote Sens. 2019, 11(10), 1238; https://doi.org/10.3390/rs11101238 - 24 May 2019

Cited by 102 | Viewed by 12932

Abstract

This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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16 pages, 12686 KiB

Open AccessArticle

Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

by Nianxu Xu, Jia Tian, Qingjiu Tian, Kaijian Xu and Shaofei Tang

Remote Sens. 2019, 11(10), 1192; https://doi.org/10.3390/rs11101192 - 19 May 2019

Cited by 38 | Viewed by 8445

Abstract

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R²s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R² = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R² = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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17 pages, 1266 KiB

Open AccessArticle

Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland

by Darío Domingo, Hans Ole Ørka, Erik Næsset, Daud Kachamba and Terje Gobakken

Remote Sens. 2019, 11(8), 948; https://doi.org/10.3390/rs11080948 - 19 Apr 2019

Cited by 42 | Viewed by 6863

Abstract

Unmanned aerial systems (UASs) and photogrammetric structure from motion (SFM) algorithms can assist in biomass assessments in tropical countries and can be a useful tool in local greenhouse gas accounting. This study assessed the influence of image resolution, camera type and side overlap on prediction accuracy of biomass models constructed from ground-based data and UAS data in miombo woodlands in Malawi. We compared prediction accuracy of models reflecting two different image resolutions (10 and 15 cm ground sampling distance) and two camera types (NIR and RGB). The effect of two different side overlap levels (70 and 80%) was also assessed using data from the RGB camera. Multiple linear regression models that related the biomass on 37 field plots to several independent 3-dimensional variables derived from five UAS acquisitions were constructed. Prediction accuracy quantified by leave-one-out cross validation increased when using finer image resolution and RGB camera, while coarser resolution and NIR data decreased model prediction accuracy, although no significant differences were observed in absolute prediction error around the mean between models. The results showed that a reduction of side overlap from 80 to 70%, while keeping a fixed forward overlap of 90%, might be an option for reducing flight time and cost of acquisitions. Furthermore, the analysis of terrain slope effect in biomass predictions showed that error increases with steeper slopes, especially on slopes greater than 35%, but the effects were small in magnitude. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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16 pages, 1826 KiB

Open AccessArticle

Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion

by Tom Swinfield, Jeremy A. Lindsell, Jonathan V. Williams, Rhett D. Harrison, Agustiono, Habibi, Elva Gemita, Carola B. Schönlieb and David A. Coomes

Remote Sens. 2019, 11(8), 928; https://doi.org/10.3390/rs11080928 - 17 Apr 2019

Cited by 47 | Viewed by 6772

Abstract

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (>10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location (

R^{2}

= 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced

R^{2}

. The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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16 pages, 5131 KiB

Open AccessArticle

Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

by Wenjian Ni, Jiachen Dong, Guoqing Sun, Zhiyu Zhang, Yong Pang, Xin Tian, Zengyuan Li and Erxue Chen

Remote Sens. 2019, 11(7), 889; https://doi.org/10.3390/rs11070889 - 11 Apr 2019

Cited by 22 | Viewed by 4618

Abstract

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R² higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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19 pages, 4899 KiB

Open AccessArticle

UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?

by Julián Tomaštík, Martin Mokroš, Peter Surový, Alžbeta Grznárová and Ján Merganič

Remote Sens. 2019, 11(6), 721; https://doi.org/10.3390/rs11060721 - 26 Mar 2019

Cited by 160 | Viewed by 13559

Abstract

Mapping hard-to-access and hazardous parts of forests by terrestrial surveying methods is a challenging task. Remote sensing techniques can provide an alternative solution to such cases. Unmanned aerial vehicles (UAVs) can provide on-demand data and higher flexibility in comparison to other remote sensing techniques. However, traditional georeferencing of imagery acquired by UAVs involves the use of ground control points (GCPs), thus negating the benefits of rapid and efficient mapping in remote areas. The aim of this study was to evaluate the accuracy of RTK/PPK (real-time kinematic, post-processed kinematic) solution used with a UAV to acquire camera positions through post-processed and corrected measurements by global navigation satellite systems (GNSS). To compare this solution with approaches involving GCPs, the accuracies of two GCP setup designs (4 GCPs and 9 GCPs) were evaluated. Additional factors, which can significantly influence accuracies were also introduced and evaluated: type of photogrammetric product (point cloud, orthoimages and DEM) vegetation leaf-off and leaf-on seasonal variation and flight patterns (evaluated individually and as a combination). The most accurate results for both horizontal (X and Y dimensions) and vertical (Z dimension) accuracies were acquired by the UAV RTK/PPK technology with RMSEs of 0.026 m, 0.035 m and 0.082 m, respectively. The PPK horizontal accuracy was significantly higher when compared to the 4GCP and 9GCP georeferencing approach (p < 0.05). The PPK vertical accuracy was significantly higher than 4 GCP approach accuracy, while PPK and 9 GCP approach vertical accuracies did not differ significantly (p = 0.96). Furthermore, the UAV RTK/PPK accuracy was not influenced by vegetation seasonal variation, whereas the GCP georeferencing approaches during the vegetation leaf-off season had lower accuracy. The use of the combined flight pattern resulted in higher horizontal accuracy; the influence on vertical accuracy was insignificant. Overall, the RTK/PPK technology in combination with UAVs is a feasible and appropriately accurate solution for various mapping tasks in forests. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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28 pages, 3157 KiB

Open AccessArticle

UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?

by Martin B. Bagaram, Diego Giuliarelli, Gherardo Chirici, Francesca Giannetti and Anna Barbati

Remote Sens. 2018, 10(9), 1397; https://doi.org/10.3390/rs10091397 - 2 Sep 2018

Cited by 67 | Viewed by 10932

Abstract

Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m² in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m²) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R² from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R² ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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22 pages, 2449 KiB

Open AccessArticle

Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

by Patrick Shin, Temuulen Sankey, Margaret M. Moore and Andrea E. Thode

Remote Sens. 2018, 10(8), 1266; https://doi.org/10.3390/rs10081266 - 11 Aug 2018

Cited by 68 | Viewed by 8008

Abstract

Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R²) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R² = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R² = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Full article

(This article belongs to the Special Issue UAV Applications in Forestry)

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