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Saukkola, Atte, Timo Melkas, Kirsi Riekki, et al. "Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data." Remote Sensing 11, no. 7 (2019): 797. http://dx.doi.org/10.3390/rs11070797.
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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 (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). 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 (Dg) and basal-area weighted mean height (Hg) 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 XYHH 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 (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.
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Cristea, Cătălina, and Andreea Florina Jocea. "Applications Of Terrestrial Laser Scanning And GIS In Forest Inventory." Journal of Applied Engineering Sciences 5, no. 2 (2015): 13–20. http://dx.doi.org/10.1515/jaes-2015-0016.
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Abstract During last years the need of knowing the forest in its various aspects, quantitative and qualitative, has enabled the appearance of a new technique forestry geomatics. Named as “the science of future” this technique integrates multiple technologies such as Remote Sensing, Airborne Photogrammetry, LIDAR, Geographic Information System (GIS), Global Positioning Systems (GPS) or classical geodetic technology for data acquisition, data processing, data analysis and data management. The purpose is to provide specific information regarding the evaluation natural forestry resources. In this paper will be presented the utilization of terrestrial 3D laser scanner and GIS technologies in forestry inventory.
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Jurjević, Luka, Mateo Gašparović, Xinlian Liang, and Ivan Balenović. "Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest." Remote Sensing 13, no. 11 (2021): 2063. http://dx.doi.org/10.3390/rs13112063.
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Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.
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Sačkov, Ivan. "Forest inventory based on canopy height model derived from airborne laser scanning data." Central European Forestry Journal 68, no. 4 (2022): 224–31. http://dx.doi.org/10.2478/forj-2022-0013.
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Abstract Airborne laser scanning (ALS) has emerged as a remote sensing technology capable of providing data suitable for deriving all types of elevation models. A canopy height model (CHM), which represents absolute height of objects above the ground in metres (e.g., trees), is the one most commonly used within the forest inventory. The aim of this study was to assess the accuracy of forest inventory performed for forest unit covered 17,583 ha (Slovakia, Central Europe) using the CHM derived from ALS data. This objective also included demonstrating the applicability of freely available data and software. Specifically, ALS data acquired during regular airborne survey, QGIS software, and packages for R environment were used for purpose of this study. A total of 180 testing plots (5.6 ha) were used for accuracy assessment. The differences between CHM-predicted and ground-observed forest stand attributes reached a relative root mean square error at 10.9%, 23.1%, and 34.5% for the mean height, mean diameter, and volume, respectively. Moreover, all predictions were unbiased (p-value < 0.05) and the strength of the relationships between CHM-predicted and ground-observed forest stand attributes were relative high (R2 = 0.7 – 0.8).
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Jamal, Juhaida, Nurul Ain Mohd Zaki, Noorfatekah Talib, et al. "Dominant Tree Species Classification using Remote Sensing Data and Object -Based Image Analysis." IOP Conference Series: Earth and Environmental Science 1019, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1755-1315/1019/1/012018.
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Abstract Over the last few decades, forests have been the victims of over logging and deforestation. Uncontrolled of this activity gave an impact to the tree species to be endangered. A detailed inventory of tree species is needed to manage and plan the forest on a sustainable basis. Many techniques had been done to identify the tree species, but in the recent three decades, remote sensing technique was widely used to study the distribution of tree species. In this study, an object-based image analysis (OBIA) with a combination of high-resolution multispectral satellite imagery (WV-2) and airborne laser scanning (LiDAR) data was tested for classification of individual tree crowns of tropical tree species at Forest Research Institute Malaysia (FRIM) forest, Selangor. LiDAR data was taken using fixed-wing aircraft with Gemini Airborne Laser Terrain Mapper (ALTM) laser with 0.15m and 0.25 resolution for horizontal and vertical. WV-2 was captured with a 0.5m spatial resolution. In this study, hyperspectral data captured using Bayspec sensor mount at UAV with height 220m from the ground and have 0.3 resolution was used to extract the spectral reflectance of tree species. Segmentation of the image was performed using multi-resolution segmentation in eCognition software. Accuracy assessment for segmentation was done by measure the ‘goodness fit’ (D value) between training object and output segmentation. The overall accuracy of the segmentation was 86%. For species classification, the accuracy assessment was performed using the error matrix confusion technique to 7 classes of tree species. The result had shown the overall accuracy classification was 64%.
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Kotivuori, Eetu, Matti Maltamo, Lauri Korhonen, Jacob L. Strunk, and Petteri Packalen. "Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches." Forestry: An International Journal of Forest Research 94, no. 4 (2021): 576–87. http://dx.doi.org/10.1093/forestry/cpab007.
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Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).
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Holopainen, M., M. Vastaranta, M. Karjalainen, et al. "FOREST INVENTORY ATTRIBUTE ESTIMATION USING AIRBORNE LASER SCANNING, AERIAL STEREO IMAGERY, RADARGRAMMETRY AND INTERFEROMETRY–FINNISH EXPERIENCES OF THE 3D TECHNIQUES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 63–69. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-63-2015.
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Three-dimensional (3D) remote sensing has enabled detailed mapping of terrain and vegetation heights. Consequently, forest inventory attributes are estimated more and more using point clouds and normalized surface models. In practical applications, mainly airborne laser scanning (ALS) has been used in forest resource mapping. The current status is that ALS-based forest inventories are widespread, and the popularity of ALS has also raised interest toward alternative 3D techniques, including airborne and spaceborne techniques. Point clouds can be generated using photogrammetry, radargrammetry and interferometry. Airborne stereo imagery can be used in deriving photogrammetric point clouds, as very-high-resolution synthetic aperture radar (SAR) data are used in radargrammetry and interferometry. ALS is capable of mapping both the terrain and tree heights in mixed forest conditions, which is an advantage over aerial images or SAR data. However, in many jurisdictions, a detailed ALS-based digital terrain model is already available, and that enables linking photogrammetric or SAR-derived heights to heights above the ground. In other words, in forest conditions, the height of single trees, height of the canopy and/or density of the canopy can be measured and used in estimation of forest inventory attributes. In this paper, first we review experiences of the use of digital stereo imagery and spaceborne SAR in estimation of forest inventory attributes in Finland, and we compare techniques to ALS. In addition, we aim to present new implications based on our experiences.
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Monnet, J. M., C. Ginzler, and J. C. Clivaz. "WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 727–31. http://dx.doi.org/10.5194/isprs-archives-xli-b8-727-2016.
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Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R<sup>2</sup>&thinsp;=&thinsp;0.61 with a root mean square error of 47&thinsp;%) and basal area (respectively 0.51 and 45&thinsp;%) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.
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Monnet, J. M., C. Ginzler, and J. C. Clivaz. "WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 727–31. http://dx.doi.org/10.5194/isprsarchives-xli-b8-727-2016.
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Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R&lt;sup&gt;2&lt;/sup&gt;&thinsp;=&thinsp;0.61 with a root mean square error of 47&thinsp;%) and basal area (respectively 0.51 and 45&thinsp;%) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.
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Grafström, Anton, and Anna Hedström Ringvall. "Improving forest field inventories by using remote sensing data in novel sampling designs." Canadian Journal of Forest Research 43, no. 11 (2013): 1015–22. http://dx.doi.org/10.1139/cjfr-2013-0123.
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It is becoming more common that auxiliary information from remote sensing is available at the planning stage of a forest field inventory. Recent developments in sampling theory allows the inclusion of such information in the sampling design to obtain better samples and, hence, improve estimates of common forest attributes. We explain the methodology and evaluate the possibility of including data from airborne laser scanning in the sampling design. The novel designs that we use can select samples that are balanced on a set of auxiliary variables and (or) well spread in a set of auxiliary variables. The results from a simulation study with real data indicate that significant improvement is achieved.
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Ørka, Hans Ole, Marie-Claude Jutras-Perreault, Jaime Candelas-Bielza, and Terje Gobakken. "Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning." Remote Sensing 14, no. 5 (2022): 1184. http://dx.doi.org/10.3390/rs14051184.
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Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity hotspots in the forest landscape. Current methods used in delineating WKH rely on costly field inventories. Furthermore, it is well known that the surveyor introduces an error because of the subjective assessment. Remote sensing may reduce this error in a cost-efficient way. The current study develops automated methods using airborne laser scanning (ALS) data to delineate geomorphological WKH, i.e., rock walls and stream gorges. The methods were evaluated based on a complete field inventory of WKH in a 1600 ha area in south-eastern Norway. The delineated WKH showed high detection rates, minor omission errors, but high commissions errors. Combining the delineation into a map of potential WKH suitable to guide field surveyors resulted in detecting all field reference WKH, i.e., a detection rate of 100% and a commission error of 25%. It is concluded that a higher degree of automatization might be possible to improve results and increase the efficiency of WKH inventories.
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White, Joanne C., Hao Chen, Murray E. Woods, Brian Low, and Sasha Nasonova. "The Petawawa Research Forest: Establishment of a remote sensing supersite." Forestry Chronicle 95, no. 03 (2019): 149–56. http://dx.doi.org/10.5558/tfc2019-024.
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The pace of technological change in forest inventory and monitoring over the past 50 years has been remarkable, largely asa result of the increased availability of various forms of remotely sensed data. Benchmarking sites, with the requisite refer-ence and baseline data for evaluating the capacities of new technologies, algorithms, and approaches, can be extremely valu-able for sparking innovation, as well as for enabling transparent and scientifically sound assessments of technologies, newdata streams, and associated information outcomes. Herein we describe the establishment of a remote sensing supersite atthe Petawawa Research Forest (PRF) in southern Ontario, Canada, and summarize the open access datasets that have beencompiled and made available to the public. The PRF is approximately 10 000 ha in size and represents a complex assemblageof tree species and forest structures. More than 1900 data records, including multiple airborne laser scanning datasets andassociated derivatives (i.e., digital terrain model, canopy height model), airborne imagery, satellite remote sensing timeseries, and ground plot data, among others, have been made openly available for download from Canada’s National ForestInformation System. We identify issues and present opportunities associated with the establishment of a remote sensingsupersite at the PRF, as well as share some of the lessons learned to foster the establishment and open data sharing for othernational and international remote sensing supersites. The PRF supersite can be accessed from the following link: https://opendata.nfis.org/mapserver/PRF.html .
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Gao, Linghan, and Xiaoli Zhang. "Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data." Forests 12, no. 12 (2021): 1713. http://dx.doi.org/10.3390/f12121713.
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Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.
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Michałowska, Maja, and Jacek Rapiński. "A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers." Remote Sensing 13, no. 3 (2021): 353. http://dx.doi.org/10.3390/rs13030353.
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Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.
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Torres, Fernanda Magri, and Antonio Maria Garcia Tommaselli. "A LIGHTWEIGHT UAV-BASED LASER SCANNING SYSTEM FOR FOREST APPLICATION." Boletim de Ciências Geodésicas 24, no. 3 (2018): 318–34. http://dx.doi.org/10.1590/s1982-21702018000300021.
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Abstract Lightweight Unmanned Aerial Vehicles (UAVs) have become a cost effective alternative for studies which use aerial Remote Sensing with high temporal frequency requirements for small areas. Laser scanner devices are widely used for rapid tridimensional data acquisition, mainly as a complementary data source to photogrammetric surveying. Recent studies using laser scanner systems onboard UAVs for forestry inventory and mapping applications have presented encouraging results. This work describes the development and accuracy assessment of a low cost mapping platform composed by an Ibeo Lux scanner, a GNSS (Global Navigation Satellite System) antenna, an Inertial Navigation System Novatel Span-IGM-S1, integrating a GNSS receiver and an IMU (Inertial Measurement Unit), a Raspberry PI portable computer and an octopter UAV. The system was assessed in aerial mode using an UAV octopter developed by SensorMap Company. The resulting point density in a plot with trees concentration was also evaluated. The point density of this device is lower than conventional Airborne Laser Systems but the results showed that altimetric accuracy with this system is around 30 cm, which is acceptable for forest applications. The main advantages of this system are their low weight and low cost, which make it attractive for several applications.
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Lang, Mait, Allan Sims, Kalev Pärna, et al. "Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition." Forestry Studies 73, no. 1 (2020): 77–97. http://dx.doi.org/10.2478/fsmu-2020-0016.
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Abstract Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.
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Novo-Fernández, Alís, Marcos Barrio-Anta, Carmen Recondo, Asunción Cámara-Obregón, and Carlos A. López-Sánchez. "Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain." Remote Sensing 11, no. 14 (2019): 1693. http://dx.doi.org/10.3390/rs11141693.
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The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8–38.3%, 34.2–41.9% and 31.7–38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.
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Gopalakrishnan, Ranjith, Jobriath Kauffman, Matthew Fagan, et al. "Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery." Forests 10, no. 3 (2019): 234. http://dx.doi.org/10.3390/f10030234.
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Sustainable forest management is hugely dependent on high-quality estimates of forest site productivity, but it is challenging to generate productivity maps over large areas. We present a method for generating site index (a measure of such forest productivity) maps for plantation loblolly pine (Pinus taeda L.) forests over large areas in the southeastern United States by combining airborne laser scanning (ALS) data from disparate acquisitions and Landsat-based estimates of forest age. For predicting canopy heights, a linear regression model was developed using ALS data and field measurements from the Forest Inventory and Analysis (FIA) program of the US Forest Service (n = 211 plots). The model was strong (R2 = 0.84, RMSE = 1.85 m), and applicable over a large area (~208,000 sq. km). To estimate the site index, we combined the ALS estimated heights with Landsat-derived maps of stand age and planted pine area. The estimated bias was low (−0.28 m) and the RMSE (3.8 m, relative RMSE: 19.7%, base age 25 years) was consistent with other similar approaches. Due to Landsat-related constraints, our methodology is valid only for relatively young pine plantations established after 1984. We generated 30 m resolution site index maps over a large area (~832 sq. km). The site index distribution had a median value of 19.4 m, the 5th percentile value of 13.0 m and the 95th percentile value of 23.3 m. Further, using a watershed level analysis, we ranked these regions by their estimated productivity. These results demonstrate the potential and value of remote sensing based large-area site index maps.
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Imangholiloo, Mohammad, Ninni Saarinen, Markus Holopainen, Xiaowei Yu, Juha Hyyppä, and Mikko Vastaranta. "Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands." Remote Sensing 12, no. 20 (2020): 3328. http://dx.doi.org/10.3390/rs12203328.
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Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.
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Heinzel, Johannes, and Christian Ginzler. "A Single-Tree Processing Framework Using Terrestrial Laser Scanning Data for Detecting Forest Regeneration." Remote Sensing 11, no. 1 (2018): 60. http://dx.doi.org/10.3390/rs11010060.
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Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.
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Knoke, Thomas, Mengistie Kindu, Thomas Schneider, and Terje Gobakken. "Inventory of Forest Attributes to Support the Integration of Non-provisioning Ecosystem Services and Biodiversity into Forest Planning—from Collecting Data to Providing Information." Current Forestry Reports 7, no. 1 (2021): 38–58. http://dx.doi.org/10.1007/s40725-021-00138-7.
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Abstract Purpose of Review Our review provides an overview of forest attributes measurable by forest inventory that may support the integration of non-provisioning ecosystem services (ES) and biodiversity into forest planning. The review identifies appropriate forest attributes to quantify the opportunity for recreation, biodiversity promotion and carbon storage, and describes new criteria that future forest inventories may include. As a source of information, we analyse recent papers on forest inventory and ES to show if and how they address these criteria. We further discuss how mapping ES could benefit from such new criteria and conclude with three case studies illustrating the importance of selected criteria delivered by forest inventory. Recent Findings Recent studies on forest inventory focus mainly on carbon storage and biodiversity promotion, while very few studies address the opportunity of recreation. Field sampling still dominates the data collection, despite the fact that airborne laser scanning (ALS) has much improved the precision of large-scale estimates of the level of forest ES provision. However, recent inventory studies have hardly addressed criteria such as visible distance in stands, presence of open water bodies and soil damages (important for the opportunity of recreation) and naturalness (here understood as the similarity of the forest to its natural state) and habitat trees and natural clearings (important for biodiversity promotion). The problem of quantifying carbon stock changes with appropriate precision has not been addressed. In addition, the reviewed studies have hardly explored the potential of inventory information to support mapping of the demand for ES. Summary We identify challenges with estimating a number of criteria associated with rare events, relevant for both the opportunity of recreation and biodiversity promotion. These include deadwood, rare species and habitat trees. Such rare events require innovative inventory technology, such as point-transect sampling or ALS. The ALS technology needs relatively open canopies, to achieve reliable estimates for deadwood or understorey vegetation. For the opportunity of recreation, the diversity among forest stands (possibly quantified by geoinformatics) and information on the presence of open water bodies (provided by RADAR, ALS data or use of existing maps) may be important. Naturalness is a crucial criterion for native biodiversity promotion but hard to quantify and assess until now. Tree species identification would be crucial for this criterion, which is still a challenge for remote sensing techniques. Estimating carbon storage may build on biomass estimates from terrestrial samples or on remotely sensed data, but major problems exist with the precision of estimates for carbon stock changes. Recent approaches for mapping the supply side of forest ES are promising, while providing so far uncommon structural information by revised inventory concepts could be helpful also for mapping the demand for ES. We conclude that future studies must find holistic inventory management systems to couple various inventory technologies in support of the integration of non-provisioning ES and biodiversity into forest planning.
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Guerra-Hernández, Cosenza, Cardil, et al. "Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data." Forests 10, no. 10 (2019): 905. http://dx.doi.org/10.3390/f10100905.
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Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.
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Stereńczak, Krzysztof, Rafał Zapłata, Jarosław Wójcik, et al. "ALS-Based Detection of Past Human Activities in the Białowieża Forest—New Evidence of Unknown Remains of Past Agricultural Systems." Remote Sensing 12, no. 16 (2020): 2657. http://dx.doi.org/10.3390/rs12162657.
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The Białowieża Forest (BF), a unique ecosystem of historical significance in central Europe, has a long history of assumed human settlement, with at least 200 known archaeological sites (until 2016). This study uncovers new evidence of the cultural heritage of this unique forest area using Airborne Laser Scanning (ALS) technology combined with traditional archaeological field assessment methods to verify the ALS data interpretations and to provide additional evidence about the function and origin of the newly detected archaeological sites. The results of this study include (1) a scientific approach for an improved identification of archaeological resources in forest areas; (2) new evidence about the history of the human use of the BF based on ALS data, covering the entire Polish part of the BF; and (3) an improved remote sensing infrastructure, supporting existing GIS (Geographic Information System) systems for the BF, a famous UNESCO Heritage site. Our study identified numerous locations with evidence of past human agricultural activities known in the literature as “field systems”, “lynchets” and “Celtic fields”. The initial identification included more than 300 km of possible field boundaries and plough headlands, many of which we have verified on the ground. Various past human activities creating those boundaries have existed since the (pre-) Roman Period up to the 13th century AD. The results of this study demonstrate that past human activities in the Polish part of the Białowieża Forest had been more prevalent than previously believed. As a practical result of the described activities, a geodatabase was created; this has practical applications for the system of monument protection in Poland, as well as for local communities and the BF’s management and conservation. The more widely achieved results are in line with the implementation of the concept of a cultural heritage inventory in forested and protected areas—the actions taken specify (built globally) the forms of protection and management of cultural and environmental goods.
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Usoltsev, Vladimir А., Ivan S. Tsepordey, and Igor M. Danilin. "Designing a Model of the Picea L. and Abies L. Biomass for Regional Climatic Conditions in Eurasia." Lesnoy Zhurnal (Forestry Journal), no. 6 (December 10, 2022): 38–54. http://dx.doi.org/10.37482/0536-1036-2022-6-38-54.
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Forest ecosystems play a major role in climate stabilization, and continuous monitoring of their biomass is of paramount importance. Airborne laser scanning technology has become widespread in assessing tree biomass by remote sensing of such inventory indices of trees and stands as crown width and projection area, tree and stand height. The work uses the author’s observation database of 1550 model trees of spruce (Picea L.) and 535 model trees of fir (Abies L.) growing in Eurasia. The database provides information on tree height, crown width and length, as well as biomass of trunk, foliage, branches and roots. It was found that two-factor allometric models of biomass components, including crown width and tree height as independent variables, are the most informative. A positive correlation with crown width and tree height has been identified for all biomass components. Biomass of components of similar-sized fir trees was found to be 45–71 % higher than that of spruce at the statistically reliable level. This is possibly due to the fact that with the same height the crown width of the fir is 11 % less compared to the spruce. The contribution of winter temperatures and precipitation to the variability of biomass components was estimated. The biomass of all components of equal-sized trees is described by a propeller-like 3D dependence. In warm regions, the biomass increases with increasing precipitation; the dependence is characterized by an opposite or neutral trend with the transition to cold regions. The biomass increases with increasing temperature in humid regions, and decreases with the transition to dry climatic conditions. Inclusion of crown length as an additional independent variable in the allometric model practically did not improve its predictive ability. The contribution of inventory indices of trees, their species, and climatic variables to explaining the variability of biomass components is 72.9, 13.5 and 13.6 %, respectively. Climatic variables explain most of the variability in trunk and above-ground biomass (18 to 20 %), least of all in needles and branches (8 to 9 %). The findings can be useful for laser monitoring of forest biomass and predicting possible changes in tree biomass structure in case of climatic deviations. Acknowledgments: The research was carried out within the framework of the state assignment FEUZ-2021-0014. For citation: Usoltsev V.А., Tsepordey I.S., Danilin I.M. Designing a Model of the Picea L. and Abies L. Biomass for Regional Climatic Conditions in Eurasia. Lesnoy Zhurnal = Russian Forestry Journal, 2022, no. 6, pp. 38–54. (In Russ.). https://doi.org/10.37482/0536-1036-2022-6-38-54
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Krok, Grzegorz, Bartłomiej Kraszewski, and Krzysztof Stereńczak. "Application of terrestrial laser scanning in forest inventory – an overview of selected issues." Forest Research Papers 81, no. 4 (2020): 175–94. http://dx.doi.org/10.2478/frp-2020-0021.
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AbstractPrecise determination of forest resources is one of the most important tasks in conducting sustainable forest management. Accurate information about the forest’s resources allows for a better planning of current and future management as well as conservation activities. Such precise information is needed by both, individual forest managers and for developing the national forest policy. In recent years, interest in the use of remote sensing in forest inventory has significantly increased. Remote sensing allows for non-invasive measurements and the automation of data processing. The most accurate source of remote sensing data at the level of the sample plot is terrestrial laser scanning (TLS). Its use in forest inventory has been studied for about two decades.This paper aims to introduce studies on state of the art TLS technology as well as provide an overview of research conducted in stands within the temperate climate zone. This article furthermore discusses issues such as TLS data acquisition, data processing and presents results for the estimation of tree biometric features.
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Wulder, Michael A., Christopher W. Bater, Nicholas C. Coops, Thomas Hilker, and Joanne C. White. "The role of LiDAR in sustainable forest management." Forestry Chronicle 84, no. 6 (2008): 807–26. http://dx.doi.org/10.5558/tfc84807-6.
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Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing. Key words: LiDAR, airborne laser altimetry, forest inventory, height, volume, biomass, update, remote sensing
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Xu, Dandan, Haobin Wang, Weixin Xu, Zhaoqing Luan, and Xia Xu. "LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives." Forests 12, no. 5 (2021): 550. http://dx.doi.org/10.3390/f12050550.
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Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.
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Persson, Henrik Jan, and Göran Ståhl. "Characterizing Uncertainty in Forest Remote Sensing Studies." Remote Sensing 12, no. 3 (2020): 505. http://dx.doi.org/10.3390/rs12030505.
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This discussion paper addresses (1) the challenge of concisely reporting uncertainties in forest remote sensing (RS) studies, primarily conducted at plot and stand level, and (2) the influence of reference data errors and how corrections for such errors can be made. Different common ways of reporting uncertainties are discussed, and a parametric error model is proposed as a core part of a comprehensive approach for reporting uncertainties (compared to, e.g., conventional reporting of root mean square error (RMSE)). The importance of handling reference data errors is currently increasing since estimates derived from RS data are becoming increasingly accurate; in extreme cases the accuracies of RS- and field-based estimates are of equal magnitude and there is a risk that reported RS accuracies are severely misjudged due to inclusion of errors from the field reference data. Novel methods for correcting for some types of reference data errors are proposed, both for the conventional RMSE uncertainty metric and for the case when a parametric error model is applied. The theoretical framework proposed in this paper is demonstrated using real data from a typical RS study where airborne laser scanning and synthetic aperture radar (SAR) data are applied for estimating biomass at the level of forest stands. With the proposed correction method, the RMSE for the RS-based estimates from laser scanning was reduced from 50.5 to 49.5 tons/ha when errors in the field references were properly accounted for. The RMSE for the estimates from SAR data was reduced from 28.5 to 26.1 tons/ha.
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Cameron, H. A., D. Schroeder, and J. L. Beverly. "Predicting black spruce fuel characteristics with Airborne Laser Scanning (ALS)." International Journal of Wildland Fire 31, no. 2 (2021): 124–35. http://dx.doi.org/10.1071/wf21004.
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Wildfire decision support systems combine fuel maps with other fire environment variables to predict fire behaviour and guide management actions. Until recently, financial and technological constraints have limited provincial fuel maps to relatively coarse spatial resolutions. Airborne Laser Scanning (ALS), a remote sensing technology that uses LiDAR (Light Detection and Ranging), is becoming an increasingly affordable and pragmatic tool for mapping fuels across localised and broad areas. Few studies have used ALS in boreal forest regions to describe structural attributes such as fuel load at a fine resolution (i.e. <100 m2 cell resolution). We used ALS to predict five forest attributes relevant to fire behaviour in black spruce (Picea mariana) stands in Alberta, Canada: canopy bulk density, canopy fuel load, stem density, canopy height and canopy base height. Least absolute shrinkage and selection operator (lasso) regression models indicated statistically significant relationships between ALS data and the forest metrics of interest (R2 ≥0.81 for all metrics except canopy base height which had a R2 value of 0.63). Performance of the regression models was acceptable and consistent with prior studies when applied to test datasets; however, regression models presented in this study mapped stand attributes at a much finer resolution (40 m2).
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Essery, Richard, Peter Bunting, Aled Rowlands, et al. "Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sensing." Journal of Hydrometeorology 9, no. 2 (2008): 228–41. http://dx.doi.org/10.1175/2007jhm870.1.
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Abstract Solar radiation beneath a forest canopy can have large spatial variations, but this is frequently neglected in radiative transfer models for large-scale applications. To explicitly model spatial variations in subcanopy radiation, maps of canopy structure are required. Aerial photography and airborne laser scanning are used to map tree locations, heights, and crown diameters for a lodgepole pine forest in Colorado as inputs to a spatially explicit radiative transfer model. Statistics of subcanopy radiation simulated by the model are compared with measurements from radiometer arrays, and scaling of spatial statistics with temporal averaging and array size is discussed. Efficient parameterizations for spatial averages and standard deviations of subcanopy radiation are developed using parameters that can be obtained from the model or hemispherical photography.
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Hu, Yang, Fayun Wu, Zhongqiu Sun, et al. "The Laser Vegetation Detecting Sensor: A Full Waveform, Large-Footprint, Airborne Laser Altimeter for Monitoring Forest Resources." Sensors 19, no. 7 (2019): 1699. http://dx.doi.org/10.3390/s19071699.
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The use of satellite-borne large-footprint LiDAR (light detection and ranging) systems allows for the acquisition of forest monitoring data. This paper mainly describes the design, use, operating principles, installation and data properties of the new Laser Vegetation Detecting Sensor (LVDS), a LiDAR system designed and developed at the Academy of Forest Inventory and Planning (AFIP) and the Beijing Institute of Telemetry (BIT). Data from LVDS were used to calculate the mean height of forest trees on sample plots using data collected in the Hunan province of China. The results show that the full waveform data obtained by LVDS has the ability to accurately characterize forest height. The mean absolute percentage error of mean forest height per plot in flat areas was 6.8%, with a mean absolute deviation of 0.78 m. The airborne LVDS system provides prototype data sets and a platform for instrument proof-of-concept studies for China’s Terrestrial Ecosystem Carbon Monitoring (TECM) mission, which is an Earth remote sensing satellite due for launch in 2020. The information produced by LVDS allows for forest structure studies with high accuracy and coverage of large areas.
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Lefsky, M. A., W. B. Cohen, and T. A. Spies. "An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon." Canadian Journal of Forest Research 31, no. 1 (2001): 78–87. http://dx.doi.org/10.1139/x00-142.
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This research evaluates the utility of several remote sensing data types for the purpose of mapping forest structure and related attributes at a regional scale. Several sensors were evaluated, including (i) single date Landsat Thematic Mapper (TM); (ii) multitemporal Landsat TM; (iii) Airborne Data Acquisition and Registration (ADAR), a sensor with high spatial resolution; (iv) Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), a sensor with high spectral resolution; and (v) Scanning Lidar Imager Of Canopies By Echo Recovery (SLICER), a lidar sensor that directly measures the height and canopy structure of forest vegetation. To evaluate the ability of each of the sensors to predict stand structure attributes, we assembled a data set consisting of 92 field plots within the Willamette National Forest in the vicinity of the H.J. Andrews Experimental Forest. Stand structure attributes included age, basal area, aboveground biomass, mean diameter at breast height (DBH) of dominant and codominant stems, mean and standard deviation of the DBH of all stems, maximum height, and the density of stems with DBH greater than 100 cm. SLICER performed better than any other remote sensing system in its predictions of forest structural attributes. The performance of the imaging sensors (TM, multitemporal TM, ADAR, and AVIRIS) varied with respect to which forest structural variables were being examined. For one group of variables there was little difference in the ability of the these sensors to predict forest structural attributes. For the remaining variables, we found that multitemporal TM was as or more effective than either ADAR or AVIRIS. These results indicate that multitemporal TM should be investigated as an alternative to either hyperspectral or hyperspatial sensors, which are more expensive and more difficult to process than multitemporal Landsat TM.
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Steinmann, Katharina, Christian Ginzler, and Adrian Lanz. "Kombination von Landesforstinventar- und Fernerkundungsdaten für Kleingebietsschätzungen." Schweizerische Zeitschrift fur Forstwesen 162, no. 9 (2011): 290–99. http://dx.doi.org/10.3188/szf.2011.0290.
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Combining data from the Swiss National Forest Inventory and from remote sensing for small-area estimations in forestry A design-unbiased small estimator was tested in this study. This estimator combines terrestrial data from the Swiss National Forest Inventory (LFI) with ancillary data from stereo aerial images and airborne laser scanner (ALS) data. The estimator was tested for the two target variables: the percentage of forest and the timber volume. The efficiency of the estimator depends on the model precision of the target variable obtained with remote sensing data and other ancillary spatial data, which can potentially explain the spatial variation of the target variable. Canopy heights derived from stereo aerial images (ADS40) and ALS data were used as ancillary data. Regression models for the timber volume and the forest/non-forest decision of the LFI samples were calibrated within the cantons Appenzell Inner Rhodes and Appenzell Outer Rhodes and provided a coefficient of determination of roughly 60%. Adding the forest/non-forest decision from the aerial photo interpretation of the LFI as an explanatory variable slightly improved the models and allowed to gain a coefficient of determination of 65% for the timber volume and 85% for the forest/non-forest decision. Within the forest area, the canopy height models explained nearly 40% (ALS data) and 20% (ADS40 data) of the observed timber volume variability. This case study shows that using remote sensing data can increase the precision (in terms of the standard error) of the timber volume estimation in canton Appenzell Inner Rhodes by roughly 30%. The same is valid for the estimation of the percentage of forest. A reduction in the standard error of about 10% for the latter estimation was obtained by using the aerial images and nearly 25% using the ALS data.
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Packalén, Petteri, and Matti Maltamo. "Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs." Canadian Journal of Forest Research 38, no. 7 (2008): 1750–60. http://dx.doi.org/10.1139/x08-037.
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The use of diameter distributions originates from a need for tree-level description of forest stands, which is required, for example, in growth simulators and bucking. Diameter distribution models are usually applied, since measuring empirical diameter distributions in practical forest inventories is too laborious. This study investigated the ability of remote sensing information to predict species-specific diameter distributions. The study was carried out in Finland in a typical managed boreal forest area. The tree species considered were Scots pine ( Pinus sylvestris L.), Norway spruce ( Picea abies (L.) Karst.), and deciduous trees as a group. Growing stock was estimated using the k-MSN method using airborne laser scanning data and aerial photographs. Two approaches were compared: first, the nearest neighbour approach based on field measured trees was used as such to predict diameter distribution, and second, a theoretical diameter distribution approach in which the parameters of the Weibull distribution are predicted using the k-MSN estimates was applied. Basically, all test criteria indicated that the diameter distribution based on nearest neighbour imputed trees outperforms the Weibull distribution, but care must be taken to ensure that the modelling data are comprehensive enough.
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Стариков, Aleksandr Starikov, Батурин, and Kirill Baturin. "The use of laser scanning technology of accounting for wood." Forestry Engineering Journal 5, no. 4 (2015): 114–22. http://dx.doi.org/10.12737/17409.
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Now for the decision of tasks of monitoring and evaluation of forest plantations the use of methods and means of laser scanning is one of the most act-sexual and priorities. Laser scanning can be performed independently, or in combination with digital aerial and space photos and video, and can also be carried out ground research on the sample areas. A number of indicators laser scanning is superior to other, currently known, remote evaluation methods qualitative and quantitative characteristics of the forest Fund Laser scanning of forest cover based on the use of modern tech-nologies of digital photogrammetry and geoinformation systems, as well as methods of digital processing and multidimensional modeling of the reflected signals. The article provides analysis of modern methods and means of aerial and terrestrial laser scanning of forest stands. The use of air-borne laser scanning will allow achieving high precision in the determination of basic inventory pa-rameters that are comparable to land-based taxation. Main advantages of laser ranging to other me-thods of monitoring of forest plantations is that the laser beam is able to penetrate the forest canopy, thereby scanning all the tiers of the stand. High density scanning (5-10 points per 1 m2) allows ob-taining three-dimensional images of individual trees with high accuracy. The obtained three-dimensional model requires no processing, unlike aerospace methods of remote sensing that are as-sociated with long and arduous races-encryption of the images. Terrestrial laser scanning, in fact, similar to traditional photogrammetric methods, but it allows you to get the coordinates from one point of standing with the possibility of control measurements directly in the field, while providing higher measurement accuracy, compared with photogrammetric methods.
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Chi, Dengkai, Jeroen Degerickx, Kang Yu, and Ben Somers. "Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy." Remote Sensing 12, no. 15 (2020): 2435. http://dx.doi.org/10.3390/rs12152435.
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Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to urban trees has not yet been fully explored—due to the presence of heterogeneous tree species and backgrounds, severely complicating the classification of tree health using remote sensing information. In this study, tree health was represented by a set of field-assessed tree health indicators (defoliation, discoloration, and a combination thereof), which were classified using airborne laser scanning (ALS) and hyperspectral imagery (HSI) with a Random Forest classifier. Different classification scenarios were established aiming at: (i) Comparing the performance of ALS data, HSI and their combination, and (ii) examining to what extent tree species mixtures affect classification accuracy. Our results show that although the predictive power of ALS and HSI indices varied between tree species and tree health indicators, overall ALS indices performed better. The combined use of both ALS and HSI indices results in the highest accuracy, with weighted kappa coefficients (Kc) ranging from 0.53 to 0.79 and overall accuracy ranging from 0.81 to 0.89. Overall, the most informative remote sensing indices indicating urban tree health are ALS indices related to point density, tree size, and shape, and HSI indices associated with chlorophyll absorption. Our results further indicate that a species-specific modelling approach is advisable (Kc points improved by 0.07 on average compared with a mixed species modelling approach). Our study constitutes a basis for future urban tree health monitoring, which will enable managers to guide early remediation management.
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Woods, M., K. Lim, and P. Treitz. "Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence forest of Ontario." Forestry Chronicle 84, no. 6 (2008): 827–39. http://dx.doi.org/10.5558/tfc84827-6.
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Models were developed to predict forest stand variables for common species of the Great Lakes – St. Lawrence forest of central Ontario, Canada from light detection and ranging (LiDAR) data. Stands that had undergone various ranges of partial harvesting or initial spacing treatments from multiple geographic sites were considered. A broad forest stratification was adopted and consisted of: (i) natural hardwoods; (ii) natural conifers; and (iii) plantation conifers. Stand top height (R2 = 0.96, 0.98, and 0.98); average height (R2 = 0.86, 0.76, and 0.98); basal area (R2 = 0.80, 0.80, and 0.85); volume (R2 = 0.89, 0.81, and 0.91); quadratic mean diameter (R2 = 0.80, 0.68, and 0.83); and density (R2 = 0.74, 0.71, and 0.73) were predicted from low density (i.e., 0.5 point m-2) LiDAR data for these 3 strata, respectively. Key words: light detection and ranging, LiDAR, airborne laser scanning, forest modelling, remote sensing, forest stand variables, Great Lakes – St. Lawrence forest
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Varhola, A., G. W. Frazer, P. Teti, and N. C. Coops. "Estimation of forest structure metrics relevant to hydrologic modelling using coordinate transformation of airborne laser scanning data." Hydrology and Earth System Sciences 16, no. 10 (2012): 3749–66. http://dx.doi.org/10.5194/hess-16-3749-2012.
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Abstract. An accurate characterisation of the complex and heterogeneous forest architecture is necessary to parameterise physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and, therefore, difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote-sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimises geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression approaches to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. We expect this approach to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterising fully-distributed hydrologic models.
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Varhola, A., G. W. Frazer, P. Teti, and N. C. Coops. "Estimation of forest structure metrics relevant to hydrologic modeling using coordinate transformation of airborne laser scanning data." Hydrology and Earth System Sciences Discussions 9, no. 4 (2012): 5531–73. http://dx.doi.org/10.5194/hessd-9-5531-2012.
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Abstract. Accurate characterizations of the complex and heterogeneous forest architecture are necessary to parameterize physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and therefore difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in-situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimizes geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. This approach is expected to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterize hydrologic models.
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Orumaa, Argo, Priit Vellak, Mait Lang, Marek Metslaid, Riho Kägo, and Mart Noorma. "How Can Remote Sensing Reduce Required Human Intervention in Robotic Forest Regeneration." Forests 12, no. 12 (2021): 1802. http://dx.doi.org/10.3390/f12121802.
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In this article, we introduce an alternative solution for forest regeneration based on unmanned ground vehicles (UGV) and describe requirements for external data, which could significantly increase the level of automation. Over the past few decades, the global forested area has decreased, and there is a great need to restore and regenerate forests. Challenges such as the lack of labor and high costs demand innovative approaches for forest regeneration. Mechanization has shown satisfactory results in terms of time-efficient planting, although its usage is limited by high operational costs. Innovative technologies must be cost-efficient and profitable for large scale usage. Automation could make mechanized forest regeneration feasible. Forest regeneration operations can be automated using a purpose built unmanned platform. We developed a concept to automate forest planting operations based on mobility platform. The system requires external data for efficient mobility in clear-cut areas. We developed requirements for external data, analyzed available solutions, and experimented with the most promising option, the SfM (structure from motion) technique. Earth observation data are useful in the planning phase. A DEM (digital terrain model) for UGV planter operations can be constructed using ALS (airborne laser scanning), although it may be restricted by the cost. Low-altitude flights by drones equipped with digital cameras or lightweight laser scanners provided a usable model of the terrain. This model was precise (3–20 cm) enough for manually planning of the trajectory for the planting operation. This technique fulfilled the system requirements, although it requires further development and will have to be automated for operational use.
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KHLYUSTOV, V. K., S. A. YURCHUK, D. V. KHLYUSTOV, and A. M. GANIKHIN. "TECHNOLOGY OF INTEGRATED ASSESSMENT OF WOOD RESOURCES BY REMOTE SENSING METHODS OF THE EARTH." Prirodoobustrojstvo, no. 4 (2021): 129–38. http://dx.doi.org/10.26897/1997-6011-2021-4-129-138.
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The relevance and significance of the problem of automated forest inventory is dictated by regulatory documents defining the main directions and principles of digitalization of the country’s economic sectors, including the forest sector. The article is devoted to the problem of automated inventory of forests and digitalization of wood resources by technical means of ground-based taxation of stands, as well as remote aerial photography methods, analytical decoding of the forest canopy and determination of the complex of taxation indicators through the use of information and reference systems of multidimensional forest taxation standards. To construct an orthophotoplane and obtain a digital vegetation model, aerial photography works that meet the requirements of the photogrammetric method and the method of air-laser scanning (ALS) are described. The requirements for the parameters of aerial photography using the photogrammetric method, as well as for the parameters in the BOS, are set out. Variants of the technology of inventory of stands are proposed, indicating the appropriate tools for obtaining remote sensing data of the Earth. An assessment of the reliability of contour decoding of the species composition of stands with different spatial resolution of remote sensing data is given. The accuracy of digital vegetation models with different spatial resolution of data, the possibility of evaluating morphometric and volumetric indicators of tree crowns, as well as the resulting indicators of canopy closeness as a result of automation are indicated. An important element of the automated digitalization of wood resources is the allocation and taxation of cutting areas, the assessment of the commodity-monetary potential of stands allocated for logging.
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Girardin, Patricia, Osvaldo Valeria, and François Girard. "Measuring Spatial and Temporal Gravelled Forest Road Degradation in the Boreal Forest." Remote Sensing 14, no. 3 (2022): 457. http://dx.doi.org/10.3390/rs14030457.
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Degradation of forest roads in Canada has strong negative effects on access to forestlands, together with economic (e.g., increased maintenance costs), environmental (e.g., erosion of materials and subsequent habitat contamination), and social (e.g., use risks) impacts. Maintaining sustainable and safe access to forestland requires a better understanding and knowledge of forest road degradation over time and space. Our study aimed to identify relevant spatiotemporal variables regarding the state of eastern Canadian forest road networks by (1) building predictive models of gravel forest road degradation and assessing effects of the slope, time, loss of the road surface, and road width (field approach), and (2) evaluating the potential of topography, roughness and vegetation indices obtained from Airborne Laser Scanning (ALS) data and Sentinel-2 optical images to estimate degradation rates (remote sensing approach). The field approach (n = 207 sample plots) confirmed that only four variables were efficient to estimate degradation rates (pseudo-R2 = 0.43 with ±8% error). Simulations that were conducted showed that after about five years without maintenance, the rate of degradation on a road, regardless of its width, increased exponentially, exacerbated by a high slope gradient and loss of road surface. The narrowest roads tended to degrade more rapidly over time. The remote sensing approach performed quite well (pseudo-R2 = 0.34 with ±9% error) in terms of predicting road degradation, giving us the valuable tools to spatialise the state of gravel forest road degradation in eastern Canadian forest. This study provided new knowledge and tools that are critical for maintaining and sustaining access to Canada’s boreal forest territory in both the short- and the long-term.
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Orwig, D. A., P. Boucher, I. Paynter, E. Saenz, Z. Li, and C. Schaaf. "The potential to characterize ecological data with terrestrial laser scanning in Harvard Forest, MA." Interface Focus 8, no. 2 (2018): 20170044. http://dx.doi.org/10.1098/rsfs.2017.0044.
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Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( Tsuga canadensis ) canopy health affected by an invasive insect, the hemlock woolly adelgid ( Adelges tsugae ). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.
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Hao, Yuanshuo, Faris Rafi Almay Widagdo, Xin Liu, Ying Quan, Lihu Dong, and Fengri Li. "Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning." Remote Sensing 13, no. 1 (2020): 24. http://dx.doi.org/10.3390/rs13010024.
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Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.
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Tóth, Sándor F. "The 17th Symposium on Systems Analysis in Forest Resources: An Introduction and Synthesis." Forest Science 66, no. 4 (2020): 424–27. http://dx.doi.org/10.1093/forsci/fxaa010.
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Abstract The 17th Symposium on Systems Analysis in Forest Resources was held in Suquamish, Washington, United States on August 27–30, 2017. The goal of this international meeting was to bring together operations researchers, remote sensing scientists, and the government to facilitate the exchange and implementation of systems science in forestry and conservation. The essay that follows is a summary of the outcome of the Symposium, as well as an introduction to the eight research articles that were selected for publication in this Special Issue. Each of the papers was presented at the Symposium and has undergone rigorous peer review. The papers represent a broad disciplinary scope within system analysis ranging from forest economics and management science to remote sensing. The problems addressed within these disciplines also vary, from wildfire mitigation, supply-chain optimization, bioenergy logistics, and participatory forest planning to fuel assessment. The technical tools the authors applied to these problems are equally diverse: game theory, dynamic programming, stochastic optimization, multiobjective decision theory, structure-from-motion, and airborne laser scanning.
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Homolová, Lucie, Růžena Janoutová, Petr Lukeš, et al. "In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž." Beskydy 10, no. 1-2 (2017): 75–86. http://dx.doi.org/10.11118/beskyd201710010075.
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Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.
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Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, et al. "Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning." Biogeosciences 15, no. 12 (2018): 3811–30. http://dx.doi.org/10.5194/bg-15-3811-2018.
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Abstract. Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62 % of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.
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Szporak-Wasilewska, Sylwia, Hubert Piórkowski, Wojciech Ciężkowski, Filip Jarzombkowski, Łukasz Sławik, and Dominik Kopeć. "Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data." Remote Sensing 13, no. 8 (2021): 1504. http://dx.doi.org/10.3390/rs13081504.
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The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F1-SCORE equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F1-SCORE equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F1-SCORE equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy.
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Dobre, Alexandru Claudiu, Ionuț-Silviu Pascu, Ștefan Leca, et al. "Applications of TLS and ALS in Evaluating Forest Ecosystem Services: A Southern Carpathians Case Study." Forests 12, no. 9 (2021): 1269. http://dx.doi.org/10.3390/f12091269.
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Forests play an important role in biodiversity conservation, being one of the main providers of ecosystem services, according to the Economics of Ecosystems and Biodiversity. The functions and ecosystem services provided by forests are various concerning the natural capital and the socio-economic systems. Past decades of remote-sensing advances make it possible to address a large set of variables, including both biophysical parameters and ecological indicators, that characterize forest ecosystems and their capacity to supply services. This research aims to identify and implement existing methods that can be used for evaluating ecosystem services by employing airborne and terrestrial stationary laser scanning on plots from the Southern Carpathian mountains. Moreover, this paper discusses the adaptation of field-based approaches for evaluating ecological indicators to automated processing techniques based on airborne and terrestrial stationary laser scanning (ALS and TLS). Forest ecosystem functions, such as provisioning, regulation, and support, and the overall forest condition were assessed through the measurement and analysis of stand-based biomass characteristics (e.g., trees’ heights, wood volume), horizontal structure indices (e.g., canopy cover), and recruitment-mortality processes as well as overall health status assessment (e.g., dead trees identification, deadwood volume). The paper, through the implementation of the above-mentioned analyses, facilitates the development of a complex multi-source monitoring approach as a potential solution for assessing ecosystem services provided by the forest, as well as a basis for further monetization approaches.
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Muumbe, Tasiyiwa Priscilla, Jussi Baade, Jenia Singh, Christiane Schmullius, and Christian Thau. "Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas." Remote Sensing 13, no. 3 (2021): 507. http://dx.doi.org/10.3390/rs13030507.
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Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.