Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data

Forest resource inventory is a significant part of the sustainable management of forest ecosystems. Finding methods to accurately estimate the diameter at breast height (DBH), tree height and tree position is a significant part of forest resource inventory. The traditional methods of forest resource inventory are expensive, difficult, laborious and time-consuming; the existing systems are not convenient to carry, resulting in low working efficiency. In addition, it is usually necessary to rely on a forest compass, DBH taper and RTK or handheld GPS to set up the plot. These instruments each have a single function and cannot achieve accurate positioning under the forest canopy. Therefore, it is necessary to update the existing equipment and technology. This study aimed to design. a multi-functional, high-precision, real-time. positioning intelligent tree-measuring instrument that integrates plot the set-up, DBH measurement, tree height measurement and tree position measurement. The instrument is based on the ultra-wideband positioning principle, sensor technology, image processing technology, trigonometric functions, tree surveying and other related theories and realizes the functions of plot set-up, tree position measurement, DBH measurement, tree height measurement and other functions. The device was tested in four square plots. The results showed that the root mean squared. error (RMSE). of the tree position estimates ranged from 0.07 m to 0.16 m, while the relative root mean squared error (rRMSE) of the DBH estimates of individual trees ranged from 3.01 to 6.43%, which is acceptable for practical applications in traditional forest inventory. The rRMSE of the tree height estimates ranged from 3.47 to 5.21%. Furthermore, the cost of this instrument is only about one-third that of traditional forestry survey tools, while the work efficiency is three times that of the traditional measurement methods. Overall, the results confirmed that the tree measuring instrument is a practical tool for obtaining. accurate measurements of the tree position, DBH and tree height for forest inventories.

Forest management is the management of private or public forest resources to achieve their conservation, social services, and economic values, concerned with the administrative, economic, legal and social aspects. All decision-making, operations-scheduling, and policy-planning require information of high quality. In forest management, this information is acquired by means of forest inventory: the systematic collection of data and information derived from forest measurements. A forest inventory is not only used for estimating the current growing stock, also conducted at several points of time in order to analyse temporal changes and yield forecasting. When conducting a forest inventory several forest parameters need to be taken into account, including individual tree heights, site quality, diameter at breast height, basal area, stocking, and timber volume. The main purpose of forest inventory is to measure these forest characteristics for estimating means and totals of timber products and planning harvest over a defined area (Kangas and Maltamo, 2006). However, it is infeasible to measure all individual trees (whole forest) in a large-scale region; therefore the acquisition of forest attributes is based on sampling. Typically, forest inventory is usually implemented by measuring the sample plots in the field, a proportion of the whole population of trees, to estimate the extent, quantity and condition of the whole forest. Thus, forest inventory in a large-scale plantation based on sampling involves time consuming and labour intensive field data collection. The development of remote sensing techniques makes it possible to conduct large-scale forest surveys with three-dimensional information at various scales from the forest stand level to individual tree level. Particularly, LiDAR (Light Detection and Ranging), an active remote sensing technique, emerges as rapid and efficient tool for forest inventories. It offers the ability to measure forest attributes at the individual tree level. This thesis aims to explore the potential of LiDAR data for automated forest inventory estimates. An integrated GIS tool was developed for constructing a forest inventory system for Pinus radiata plantations in Victoria, Australia. The tool was built as a set of tools running on the desktop GIS software package ArcGIS by integrating spatial analysis, LiDAR data analysis and image segmentation techniques as well as empirical tree models to support forest inventories of Pinus radiata on an individual tree basis. It provides functions for selecting forest plots to extract LiDAR data, building canopy height models (CHM) from the extracted LiDAR data, delineating individual trees on the CHMs by applying the marker controlled watershed segmentation technique, and deriving forest inventory estimates based on the CHMs and identified individual trees through spatial analysis and tree modelling using the empirical models. The integrated GIS tool was applied to a forest inventory of Pinus radiata plantations in Mt. Worth, Victoria, managed by HVP Pty Limited. The inventory results were validated using the field survey data. The tool not only provides a practical means of forest inventory of Pinus radiata plantations in southern Australia, but also a new approach to the development of a fully automated forest inventory system through the integration of advanced GIS and LiDAR technology.

The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance.

Measuring diameter at breast height (DBH) is an essential but laborious task in the traditional forest inventory; it motivates people to develop alternative methods based on remote sensing technologies. In recent years, structure from motion (SfM) photogrammetry has drawn researchers’ attention in forest surveying for its economy and high precision as the light detection and ranging (LiDAR) methods are always expensive. This study explores an automatic DBH measurement method based on SfM. Firstly, we proposed a new image acquisition technique that could reduce the number of images for the high accuracy of DBH measurement. Secondly, we developed an automatic DBH estimation pipeline based on sample consensus (RANSAC) and cylinder fitting with the Least Median of Squares with impressive DBH estimation speed and high accuracy comparable to methods based on LiDAR. For the application of SfM on forest survey, a graphical interface software Auto-DBH integrated with SfM reconstruction and automatic DBH estimation pipeline was developed. We sampled four plots with different species to verify the performance of the proposed method. The result showed that the accuracy of the first two plots, where trees’ stems were of good roundness, was high with a root mean squared error (RMSE) of 1.41 cm and 1.118 cm and a mean relative error of 4.78% and 5.70%, respectively. The third plot’s damaged trunks and low roundness stems reduced the accuracy with an RMSE of 3.16 cm and a mean relative error of 10.74%. The average automatic detection rate of the trees in the four plots was 91%. Our automatic DBH estimation procedure is relatively fast and on average takes only 2 s to estimate the DBH of a tree, which is much more rapid than direct physical measurements of tree trunk diameters. The result proves that Auto-DBH could reach high accuracy, close to terrestrial laser scanning (TLS) in plot scale forest DBH measurement. Our successful application of automatic DBH measurement indicates that SfM is promising in forest inventory.

Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes.Area of study: Monte Morello, Tuscany region, ItalyMaterial and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested.Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R2=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R2=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations.Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees.

Accurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory.

Forest monitoring tools are needed to promote effective and data driven forest management and forest policies. Remote sensing techniques can increase the speed and the cost-efficiency of the forest monitoring as well as large scale mapping of forest attribute (wall-to-wall approach). Digital Aerial Photogrammetry (DAP) is a common cost-effective alternative to airborne laser scanning (ALS) which can be based on aerial photos routinely acquired for general base maps. DAP based on such pre-existing dataset can be a cost effective source of large scale 3D data. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photogrammetric Canopy Height Model (pCHM) which describes the tree canopy height. While this potential seems pretty obvious, few studies have investigated the quality of regional pCHM based on aerial stereo images acquired by standard official aerial surveys. Our study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filed-measured tree height database. To further ensure the replicability of the approach, the pCHM tree height estimates benchmarking only relied on public forest inventory (FI) information and the photogrammetric protocol was based on low-cost and widely used photogrammetric software. Moreover, our study investigates the relationship between the pCHM tree height estimates based on the neighboring forest parameter provided by the FI program.Our results highlight the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates. Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters like tree size (diameter at breast height) and tree type (evergreenness/deciduousness) as well as the terrain topography (slope) to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate, the terrain slope, the Diameter at Breast Height (DBH) and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0.90 VS 0.87) and root mean square error (RMSE, 1.78 VS 2.01 m). Such aspects are poorly addressed in literature and further research should focus on how pCHM approaches could integrate them to improve forest characterization using DAP and pCHM. Our promising results can be used to encourage the use of regional aerial orthophoto surveys archive to produce large scale quality tree height data at very low additional costs, notably in the context of updating national forest inventory programs.

Accurate estimates of forest inventory parameters are essential to assess the potential hazards of wildfire and obtain above-ground biomass and carbon sequestration data that help develop strategies for the sustainable management of forests. This study aims to assess the accuracy of estimation of forest inventory parameters, such as diameter at breast height (DBH) and tree height, obtained using a Terrestrial Laser Scanner (TLS) in a Mediterranean coniferous stand in western Greece. DBH values measured in the field were compared with those derived from a TLS using the Computree algorithm for automatic DBH detection, and resulted in a coefficient of determination ( $$R^{2})$$ that ranged from 0.75 to 0.96 at the plot level. The average $$R^{2}$$ and RMSE values of 0.80 and 1.07 m, respectively, were obtained when comparing the tree heights recorded by TLS and field data. Finally, the feasibility of TLS to estimate total dry biomass was investigated by comparing the TLS-derived total dry biomass values with those derived from field estimates using an allometric equation. The average estimate of biomass per hectare according to the TLS inventory data was 373.17 Mg/ha while that from field observations was 366.82 Mg/ha. The results confirm that TLS can provide non-destructive, high-resolution and precise determination of forest inventory parameters. The outcomes of this research will help researchers to better comprehend deviations in the accuracy of forest inventory variable retrieval resulting from the variation in the processing parameters supplied and additionally boost decision-making in forest management.

Large area forest inventory using Landsat ETM+: A geostatistical approach

Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single- and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single- and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R 2 = 0.96) and multiple scans (R 2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low root-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R 2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.

BackgroundThe importance of structurally diverse forests for the conservation of biodiversity and provision of a wide range of ecosystem services has been widely recognised. However, tools to quantify structural diversity of forests in an objective and quantitative way across many forest types and sites are still needed, for example to support biodiversity monitoring. The existing approaches to quantify forest structural diversity are based on small geographical regions or single forest types, typically using only small data sets.ResultsHere we developed an index of structural diversity based on National Forest Inventory (NFI) data of Baden-Württemberg, Germany, a state with 1.3 million ha of diverse forest types in different ownerships. Based on a literature review, 11 aspects of structural diversity were identified a priori as crucially important to describe structural diversity. An initial comprehensive list of 52 variables derived from National Forest Inventory (NFI) data related to structural diversity was reduced by applying five selection criteria to arrive at one variable for each aspect of structural diversity. These variables comprise 1) quadratic mean diameter at breast height (DBH), 2) standard deviation of DBH, 3) standard deviation of stand height, 4) number of decay classes, 5) bark-diversity index, 6) trees with DBH ≥ 40 cm, 7) diversity of flowering and fructification, 8) average mean diameter of downed deadwood, 9) mean DBH of standing deadwood, 10) tree species richness and 11) tree species richness in the regeneration layer. These variables were combined into a simple, additive index to quantify the level of structural diversity, which assumes values between 0 and 1. We applied this index in an exemplary way to broad forest categories and ownerships to assess its feasibility to analyse structural diversity in large-scale forest inventories.ConclusionsThe forest structure index presented here can be derived in a similar way from standard inventory variables for most other large-scale forest inventories to provide important information about biodiversity relevant forest conditions and thus provide an evidence-base for forest management and planning as well as reporting.

Key messageWe used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them < 10 cm in trunk diameter. This method retrieved 85% of the trees of a classic inventory. Finally, TLS revealed that stem positions of the classic inventory had geolocation errors of up to 6 m.ContextAccurate position mapping of tropical rainforest trees is crucial for baseline studies of tropical forest ecology but is labor-intensive. Terrestrial lidar scanning (TLS) is broadly used in temperate forest inventories, but its use in rainforests is restricted to the determination of individual tree volumes within small survey areas.AimsMapping tree stems across one large (12-ha) rainforest plot, including trees less than 10 cm DBH, and evaluating the precision of traditional mapping approaches.MethodsWe used lightweight TLS, co-registered the acquisitions, and developed a new efficient algorithm to process the TLS data.ResultsWe detected 36,422 stems of which 29,665 (81%) were < 10 cm in diameter at breast height (DBH). Of the trees ≥ 10 cm DBH previously censused in the plot, 85% were identified by TLS. Automatic DBH estimation from TLS data had an RMSE of 6 cm. RMSE was improved to 3 cm by a manual verification of the shape and quality of the stem points. The initial census map had substantial bias in tree geolocation with a maximum value around 6 m.ConclusionLightweight TLS technology is a promising tool for the estimation of stem tapering and volume. Here, we show that it also facilitates the establishment of large tropical forest inventories, by improving the positioning of trees, thus increasing the accuracy of forest inventories and their cost-effectiveness.

Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error &amp;lt;2 cm, bias &amp;lt;1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin.

Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds