Academic literature on the topic 'REMOTE SENSING, FOREST INVENTORY, AIRBORNE LASER SCANNING, FOREST'
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Journal articles on the topic "REMOTE SENSING, FOREST INVENTORY, AIRBORNE LASER SCANNING, FOREST"
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Saukkola, Atte, Timo Melkas, Kirsi Riekki, Sanna Sirparanta, Jussi Peuhkurinen, Markus Holopainen, Juha Hyyppä, and Mikko Vastaranta. "Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data." Remote Sensing 11, no. 7 (April 3, 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 (December 1, 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 (May 24, 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 (October 21, 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, Nurhafiza Md Saad, Ernieza Suhana Mokhtar, Hamdan Omar, Zulkiflee Abd Latif, and Mohd Nazip Suratman. "Dominant Tree Species Classification using Remote Sensing Data and Object -Based Image Analysis." IOP Conference Series: Earth and Environmental Science 1019, no. 1 (April 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 (March 24, 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, K. Karila, S. Kaasalainen, E. Honkavaara, and J. Hyyppä. "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> = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) 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<sup>2</sup> = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) 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 (November 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.
Dissertations / Theses on the topic "REMOTE SENSING, FOREST INVENTORY, AIRBORNE LASER SCANNING, FOREST"
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Guerra, Hernández Juan. "Applicability of advanced remote sensing technologies to support forest management." Doctoral thesis, ISA/UL, 2018. http://hdl.handle.net/10400.5/17507.
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Tese de Doutoramento - Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaForest ecosystems provide multiple wood and non-wood forest products and services that are crucial for the socio-economic development of rural areas. In this context, current methods of estimating variables of interest in forest ecosystems should be improved due to new demands for information related to sustainable forest management. Advanced remote sensing (RS) technologies provide data that will address the increasing demands for information and support the subsequent development of prediction models. Airborne laser scanning (ALS) has emerged as one of the most promising RS technologies for characterizing tree canopies and other biophysical characteristics essential for forest inventories. The use of 3D data acquired from Digital Aerial photography (DAP) is a useful alternative to ALS-based forest variable estimation. The rapid development of Unmanned Aerial Vehicles (UAVs) (drones) fitted with digital aerial cameras and the use of SfM (Structure from Motion) techniques together provide new possibilities for efficient mapping of forest variables. Combining ALS and DAP technologies with UAV platforms will probably have a strong impact on forest inventory practices in the next decade, leading to more accurate characterization of forest stands, as well as for monitoring forest growth. The overall aim of all of the five studies included in this doctoral thesis is to evaluate the capacity of two advanced RS technologies (ALS and DAP) to provide methods and tools that support forest management at different scales ranging from stand level to individual tree level
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Monnet, Jean-Matthieu. "Caractérisation des forêts de montagne par scanner laser aéroporté : estimation de paramètres de peuplement par régression SVM et apprentissage non supervisé pour la détection de sommets." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENT056/document.
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De nombreux travaux ont montré le potentiel de la télédétection parscanner laser aéroporté pour caractériser les massifs forestiers.Cependant, l'application aux forêts complexes de montagne reste encorepeu documentée. On se propose donc de tester les deux principalesméthodes permettant d'extraire des paramètres forestiers sur desdonnées acquises en zone montagneuse et de les adapter aux contraintesspéci fiques à cet environnement. En particulier on évaluera d'unepart l'apport conjoint de la régression à vecteurs de support et de laréduction de dimension pour l'estimation de paramètres de peuplement,et d'autre part l'intérêt d'un apprentissage non supervisé pour ladétection d'arbresNumerous studies have shown the potential of airborne laser scanningfor the mapping of forest resources. However, the application of thisremote sensing technique to complex forests encountered in mountainousareas requires further investigation. In this thesis, the two mainmethods used to derive forest information are tested with airbornelaser scanning data acquired in the French Alps, and adapted to theconstraints of mountainous environments. In particular,a framework for unsupervised training of treetop detection isproposed, and the performance of support vector regression combinedwith dimension reduction for forest stand parameters estimation isevaluated
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D'Amico, Giovanni. "Application of big data analytics in remote sensing supporting sustainable forest management." Doctoral thesis, 2022. http://hdl.handle.net/2158/1259784.
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Sustainable forest management requires detailed forest information for planning accurate treatments. The information is expected to be accurate enough and preferably obtained at a low cost and with periodic updates. Such spatial scale information is nowadays provided by remote sensing data. On the one hand, the development and use of aerial laser scanning for estimating forest variables has been a game-changer in recent decades for forest management. On the other hand, satellite remote sensing technologies, generated a constant flow of data from different platforms, in different formats and with different purposes. Combined with this ongoing remote sensing data stream, the development of computer technology has provided forest management with many new tools for data capture, data representation, data visualization, and management planning applications. Today, new computing power makes it possible to tackle the complex problem of managing and processing big data from remote sensing with new strategies that have revolutionized the way of understanding the use of these data sources.
This thesis is aimed at assessing big data analytics for practical cases of forest monitoring, especially in the Italian context, where large-scale aggregated forest remote sensing data have always been a structural lack. Four main studies were covered in the thesis. Study I involved the review and aggregation of remotely sensed forestry data at the national scale. The available Italian airborne laser scanning data were aggregated to develop a consistent mosaic of canopy heigh model, while different local forest maps were used to develop for the first time a high-resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory. An online geographic forest information system was implemented to store and facilitate the access and analysis of both spatial datasets. The two information layers were explored in operational cases, through the integration of remote sensing and inventory data in studies II and III. In the former, the forest mask produced mosaicking the Italian regional local forest maps was compared with four other forest masks available for the entire area of Italy to examine their effects on the estimation of growing stock volume and to clarify which product is best suited for this purpose. Non-forest pixels in each forest mask were removed from a national wall-to-wall growing stock volume map constructed using inventory and remote sensing data. The estimated Growing stock volume from each mask was compared with the official national forest inventory estimates. In the III study, airborne laser scanning coverage and the forest mask were used in combination with Landsat spectral data for large-scale volume estimation. Estimates were performed considering different proportions between airborne laser scanning and Landsat coverage. The integration between satellite spectral data and airborne laser scanning information is particularly critical in countries like Italy, where wall-to-wall airborne laser scanning coverage is still lacking. In the last study (IV), Sentinel-2 multitemporal data were used to identify poplar plantations, which are the primary source of Italian industrial timber. The study area was the dynamic agricultural area of Pianura Padana where most of the Italian poplar plantations are concentrated. The capabilities of the Sentinel-2 data were integrated with a deep learning approach that provided better results compared to traditional logistic regression. The map we produced can allow the poplar plantation monitoring, which requires frequent updating, not feasible with traditional forest inventories. In so doing, these studies, aimed at enhancing knowledge about missing information layers at the national scale, attempting to close the gaps underlined by previous studies.
Book chapters on the topic "REMOTE SENSING, FOREST INVENTORY, AIRBORNE LASER SCANNING, FOREST"
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Morsdorf, Felix, Fabian D. Schneider, Carla Gullien, Daniel Kükenbrink, Reik Leiterer, and Michael E. Schaepman. "The Laegeren Site: An Augmented Forest Laboratory." In Remote Sensing of Plant Biodiversity, 83–104. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_4.
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AbstractGiven the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity.
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Ferrara, Roberto, Stefano Arrizza, Andrea Ventura, Bachisio Arca, Michele Salis, Angelo Arca, Pierpaolo Masia, Pierpaolo Duce, and Grazia Pellizzaro. "Structure characterization on Mediterranean forest stand using terrestrial laser scanning." In Advances in Forest Fire Research 2022, 385–87. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_61.
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Forest stands plays an important role in Western Mediterranean ecosystems, their characterization it is needed for a whole comprehension of natural dynamics and for an efficient forestry management. The definition of the structural parameters of forest vegetation is a useful information for different environmental applications, such as studies on carbon dynamics, sustainable forest management, ecological studies, forest fuel studies and fire risk management. A precise description of the forest is particularly important for fire hazard mitigation planning because allows predictions of the potential fire behavior and its destructive effects. Obtaining detailed information on forest stand and canopy variables requires extensive, difficult, and laborious field campaigns. Remote and proximal sensing techniques for forest monitoring have become popular in recent decades. Specifically, Terrestrial Laser Scanner (TLS), based on Lidar technology, has demonstrated its potential to overcome the limitations of the conventional ground-based forest inventory techniques, but the accuracy and applicability of TLS techniques for estimation of tree attributes and canopy characterization, presupposes a correct separation between points representing shrubs, woody material, leaves and small branches and needs further investigations. In this work we developed and tested an automatic procedure based on the point density algorithms DBSCAN, to correctly separate points representing shrubs, woody material, leaves, and small branches at plot level, in order to identify woody material volumes, tree density and canopy cover on a forest stand. The study was carried out in several areas located in Sardinia, Italy, mainly covered by pine forest, mixed forest, and oak forest with different understory types. Destructive and non-destructive measurements were done inside circular plots of 10 m radius. TLS data sets were collected in field by multiple scanning of the plots. The 3D point clouds were processed for isolating trees, ground and understory and subsequently for separating wood from foliage. Cloud points were partitioned in cubic volumes (voxels) that were used as input to separate stand components (by applying principal component analysis) and to generate wood and no-wood clusters (by applying the point density algorithm DBSCAN). The first results obtained show that the proposed method allows to correctly identify foliage, trunk and main branches especially when the underlying layer is dominated by low herbaceous vegetation. However, further studies are needed to assess the ability of this method in forest stands characterized by high and dense undergrowth and with different species of trees.
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Sánchez-López, Nuria, Andrew T. Hudak, Luigi Boschetti, Carlos A. Silva, Benjamin C. Bright, and E. Louise Loudermilk. "A spatially explicit model of litter accumulation in fire maintained longleaf pine forest ecosystems of the Southeastern USA." In Advances in Forest Fire Research 2022, 1383–89. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_209.
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The continuity and depth of the surface fuel layer (i.e., litter and duff) are major drivers of fire spread and fuel consumption. Nevertheless, its spatial explicit quantification over relatively large areas remains unresolved: local fuel heterogeneity introduces large uncertainties in estimates derived from field-based models and sparse data samples. Besides that, the sensitivity of remote sensors to surface litter loads is limited, particularly under canopy cover. In fire-maintained pine forests of the Southeastern US, surface fuel accumulation and its distribution over the forest floor are mainly driven by vegetation productivity, decomposition, and time since fire (TSF). Traditional ecological and stand-level models provide a means to equilibrate between opposing rates of deposition and decomposition as a function of TSF at the landscape level but don’t account for spatial heterogeneity. We developed a top-down, object-based approach for wall-to-wall estimation of surface litter loads using TSF records, the ecological-based Olson model, and tree crown objects derived from airborne laser scanning (ALS) data. The approach involves, first, the spatially explicit estimation of litter production through a tree crown production model, driven by tree crown attributes extracted from the ALS point clouds, and informed by tree inventory data and allometric equations, including vegetation leaf turnover rates. Second, litter accumulation is estimated using the fire-driven Olson equation, which models accumulation progressively with time until decomposition balances deposition and a steady state of accumulation is reached. The methodology is demonstrated at several fire-maintained longleaf pine forest locations in southeastern USA, where tree inventory data, surface litter loads, prescribed fire records, and ALS data are available for testing and validation of the methodology. Comparison between preliminary modeled estimates and observed litter loads shows a relatively good agreement (RMSE=0.21 [kg m-2]; BIAS 0.07 [kg m-2]). This suggests that the proposed approach to indirectly map patterns of litter production and litter accumulation can provide a realistic means to map the continuity of the litter layer, thus overcoming the limitation of traditional ecological landscape models to account for spatial heterogeneity. This high-resolution map of litter loads will be further valuable as input to physics-based fire behavior and spread models and to improve the spatially explicit characterization of the duff layer.
Conference papers on the topic "REMOTE SENSING, FOREST INVENTORY, AIRBORNE LASER SCANNING, FOREST"
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Chan, Jonathan C. W., Michele Dalponte, Liviu Ene, Lorenzo Frizzera, Franco Miglietta, and Damiano Gianelle. "Forest species and biomass estimation using airborne laser scanning and hyperspectral images." In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080662.
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Leiterer, Reik, Reinhard Furrer, Michael E. Schaepman, and Felix Morsdorf. "Retrieval of canopy structure types for forest characterization using multi-temporal airborne laser scanning." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326357.
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Monnet, J. M., F. Berger, and J. Chanussot. "Support vector machines regression for estimation of forest parameters from airborne laser scanning data." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651702.
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Yadav, Kashi Ram, Subrata Nandy, Ritika Srinet, Raja Ram Aryal, and Michael Ying Yang. "Fusing Airborne Laser Scanning and Rapideye Sensor Parameters for Tropical Forest Biomass Estimation of Nepal." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900260.
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