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Remote Sensing Techniques for Precision Forestry

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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 (28 February 2019) | Viewed by 52397

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Prof. Dr. Markus Holopainen

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Guest Editor

Department of Forest Resource Management, University of Helsinki, Helsinki, Finland
Interests: forest biomass estimation using remotely sensed data including optical images and LiDAR data
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Prof. Dr. Juha Hyyppä

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Finish Geospatial Research Institute, 02431 Masala, Finland
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests
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Dr. Anttoni Jaakkola

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Finnish Geospatial Research Institute, Finland

Prof. Dr. Xinlian Liang

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State Key Laboratory of Surveying and Mapping Remote Sensing Information Engineering, Wuhan University, Wuhan 430072, China
Interests: geodesy and surveying; geoinformatics (GIS); remote sensing; laser scanning; terrestrial laser scanning; mobile laser scanning; 3D modelling; forest inventory
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Dr. Xiaowei Yu

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Finnish Geospatial Research Institute, Finland

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Dear Colleagues,

Currently, the forest inventory is a 2–3 billion € annual market, and is mainly based on field work. Inventory is increasingly shifting to the use of airborne laser scanning (ALS), and has already occurred in many countries; Scandinavia, Austria, and Canada are leading this development. Boreal and mountainous forests are easier to measure due to their smaller biodiversities (smaller number of species) and more-sparse forest structure. Instead of using ALS, point clouds can also be created using space-borne imagery or photogrammetry.

We define precision forestry as a method in which the characteristics of forests, treatments, biodiversity preservation, and recreational opportunities can be accurately determined, at the forest stand, plot, or individual tree level. Presently, the forest industry is also looking toward next-generation, individual tree-level techniques for forest inventory to create added value, cost savings, and new value chains. When applying the ideas of precision forestry concepts, we can achieve several economic and ecological benefits.

As Guest Editors, we would like to dedicate this Special Issue to documenting these remote sensing techniques, especially using laser scanning, in a timely manner, allowing for future precision forestry. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:

New approaches, concepts, and applications, especially using laser scanning, for individual-tree-based forest inventory
Feasibility studies with new sensors, ranging from hand-held to space-borne systems, and their applications to forestry
Combined use of images and laser scanning data for forestry
Comparison and benchmarking studies of using various sensors and/or processing methods for forestry
Point cloud processing techniques to forest informatics
Use of mobile and UAV, especially laser scanning, mapping techniques for forest inventory
New precision forestry applications

Dr. Markus Holopainen
Prof. Juha Hyyppä
Dr. Anttoni Jaakkola
Prof. Xinlian Liang
Dr. Xiaowei Yu
Guest Editors

Manuscript Submission Information

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

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

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Keywords

Airborne/Mobile/Terrestrial LiDAR, Airborne/Mobile/Terrestrial Laser Scanning
UAV imaging and laser scanning
Integration of point clouds with images
Precision forestry
Individual Tree Recognition, Detection and Approaches

Published Papers (8 papers)

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Research

17 pages, 4692 KiB

Open AccessArticle

Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods

by Salim Malek, Franco Miglietta, Terje Gobakken, Erik Næsset, Damiano Gianelle and Michele Dalponte

Remote Sens. 2019, 11(8), 949; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11080949 - 19 Apr 2019

Cited by 24 | Viewed by 3139

Abstract

Light detection and ranging (lidar) data are nowadays a standard data source in studies related to forest ecology and environmental mapping. Medium/high point density lidar data allow to automatically detect individual tree crowns (ITCs), and they provide useful information to predict stem diameter and aboveground biomass of each tree represented by a detected ITC. However, acquisition of field data is necessary for the construction of prediction models that relate field data to lidar data and for validation of such models. When working at ITC level, field data collection is often expensive and time-consuming as accurate tree positions are needed. Active learning (AL) can be very useful in this context as it helps to select the optimal field trees to be measured, reducing the field data collection cost. In this study, we propose a new method of AL for regression based on the minimization of the field data collection cost in terms of distance to navigate between field sample trees, and accuracy in terms of root mean square error of the predictions. The developed method is applied to the prediction of diameter at breast heights (DBH) and aboveground biomass (AGB) of individual trees by using their height and crown diameter as independent variables and support vector regression. The proposed method was tested on two boreal forest datasets, and the obtained results show the effectiveness of the proposed selecting strategy to provide substantial improvements over the different iterations compared to a random selection. The obtained RMSE of DBH/AGB for the first dataset was 5.09 cm/95.5 kg with a cost equal to 8256/6173 m by using the proposed multi-objective method of selection. However, by using a random selection, the RMSE was 5.20 cm/102.1 kg with a cost equal to 28,391/30,086 m. The proposed approach can be efficient in order to get more accurate predictions with smaller costs, especially when a large forest area with no previous field data is subject to inventory and analysis. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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15 pages, 1942 KiB

Open AccessArticle

Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

by Atte Saukkola, Timo Melkas, Kirsi Riekki, Sanna Sirparanta, Jussi Peuhkurinen, Markus Holopainen, Juha Hyyppä and Mikko Vastaranta

Remote Sens. 2019, 11(7), 797; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070797 - 03 Apr 2019

Cited by 27 | Viewed by 5785

Abstract

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XY_H) in global satellite navigation system and computationally improved harvester head positions (XY_HH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m²). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (D_g) and basal-area weighted mean height (H_g) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XY_HH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m². Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XY_HH) and a smallest plot size of 254 m². Instead, if only harvester positions (XY_H) are available, expanding the sample plot size to 761 m² reaches a similar accuracy to that obtained using XY_HH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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18 pages, 5609 KiB

Open AccessArticle

UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring

by Stuart Krause, Tanja G.M. Sanders, Jan-Peter Mund and Klaus Greve

Remote Sens. 2019, 11(7), 758; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070758 - 28 Mar 2019

Cited by 125 | Viewed by 9456

Abstract

The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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27 pages, 6576 KiB

Open AccessArticle

Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data

by Zhuli Xie, Yaoliang Chen, Dengsheng Lu, Guiying Li and Erxue Chen

Remote Sens. 2019, 11(2), 164; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020164 - 16 Jan 2019

Cited by 113 | Viewed by 9369

Abstract

The global availability of high spatial resolution images makes mapping tree species distribution possible for better management of forest resources. Previous research mainly focused on mapping single tree species, but information about the spatial distribution of all kinds of trees, especially plantations, is often required. This research aims to identify suitable variables and algorithms for classifying land cover, forest, and tree species. Bi-temporal ZiYuan-3 multispectral and stereo images were used. Spectral responses and textures from multispectral imagery, canopy height features from bi-temporal stereo imagery, and slope and elevation from the stereo-derived digital surface model data were examined through comparative analysis of six classification algorithms including maximum likelihood classifier (MLC), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The results showed that use of multiple source data—spectral bands, vegetation indices, textures, and topographic factors—considerably improved land-cover and forest classification accuracies compared to spectral bands alone, which the highest overall accuracy of 84.5% for land cover classes was from the SVM, and, of 89.2% for forest classes, was from the MLC. The combination of leaf-on and leaf-off seasonal images further improved classification accuracies by 7.8% to 15.0% for land cover classes and by 6.0% to 11.8% for forest classes compared to single season spectral image. The combination of multiple source data also improved land cover classification by 3.7% to 15.5% and forest classification by 1.0% to 12.7% compared to the spectral image alone. MLC provided better land-cover and forest classification accuracies than machine learning algorithms when spectral data alone were used. However, some machine learning approaches such as RF and SVM provided better performance than MLC when multiple data sources were used. Further addition of canopy height features into multiple source data had no or limited effects in improving land-cover or forest classification, but improved classification accuracies of some tree species such as birch and Mongolia scotch pine. Considering tree species classification, Chinese pine, Mongolia scotch pine, red pine, aspen and elm, and other broadleaf trees as having classification accuracies of over 92%, and larch and birch have relatively low accuracies of 87.3% and 84.5%. However, these high classification accuracies are from different data sources and classification algorithms, and no one classification algorithm provided the best accuracy for all tree species classes. This research implies the same data source and the classification algorithm cannot provide the best classification results for different land cover classes. It is necessary to develop a comprehensive classification procedure using an expert-based approach or hierarchical-based classification approach that can employ specific data variables and algorithm for each tree species class. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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30 pages, 5322 KiB

Open AccessArticle

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

by Maxime Soma, François Pimont, Sylvie Durrieu and Jean-Luc Dupuy

Remote Sens. 2018, 10(10), 1580; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101580 - 01 Oct 2018

Cited by 26 | Viewed by 3496

Abstract

Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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17 pages, 5998 KiB

Open AccessArticle

High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering

by Michele Martone, Francescopaolo Sica, Carolina González, José-Luis Bueso-Bello, Paolo Valdo and Paola Rizzoli

Remote Sens. 2018, 10(9), 1477; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091477 - 16 Sep 2018

Cited by 16 | Viewed by 3765

Abstract

In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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11 pages, 1662 KiB

Open AccessArticle

Forest Variable Estimation Using a High Altitude Single Photon Lidar System

by André Wästlund, Johan Holmgren, Eva Lindberg and Håkan Olsson

Remote Sens. 2018, 10(9), 1422; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091422 - 07 Sep 2018

Cited by 25 | Viewed by 4930

Abstract

As part of the digitalization of the forest planning process, 3D remote sensing data is an important data source. However, the demand for more detailed information with high temporal resolution and yet still being cost efficient is a challenging combination for the systems used today. A new lidar technology based on single photon counting has the possibility to meet these needs. The aim of this paper is to evaluate the new single photon lidar sensor Leica SPL100 for area-based forest variable estimations. In this study, it was found that data from the new system, operated from 3800 m above ground level, could be used for raster cell estimates with similar or slightly better accuracy than a linear system, with similar point density, operated from 400 m above ground level. The new single photon counting lidar sensor shows great potential to meet the need for efficient collection of detailed information, due to high altitude, flight speed and pulse repetition rate. Further research is needed to improve the method for extraction of information and to investigate the limitations and drawbacks with the technology. The authors emphasize solar noise filtering in forest environments and the effect of different atmospheric conditions, as interesting subjects for further research. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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22 pages, 9237 KiB

Open AccessArticle

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

by Jonathan P. Dash, Grant D. Pearse and Michael S. Watt

Remote Sens. 2018, 10(8), 1216; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081216 - 03 Aug 2018

Cited by 80 | Viewed by 10981

Abstract

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)

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