The integration of the terrestrial and airborne laser scanning technologies in the semi-automated process of retrieving selected trees and forest stand parameters Integração das tecnologias terrestre e aerotransportada de scanner laser no processo semi

The traditional forest inventory based usually on thousands of the circle plots (radius = 12.62m; area 500 sq m) set in regular network. On every inventory plot, the basic tree and forest stand parameters have to be collected using calliper and hypsometer or even human eye (i.e. crown closure) as well. The modern multifunctional forestry models requires more and more: dens and accurate data to deliver as fast as possible the precise information on the amount of wood stock or other selected forest stand and tree parameters. One of the promising remote sensing technologies is LiDAR collecting the 3D point cloud data. The TLS technology is very precise and fast but is limited to relatively small areas like forest inventory plot. The ALS is more focused on wide-area data collection. Both technologies are complementary, therefore it is a need for the fusion of those two sources of information to enhance the accuracy of tree parameters and enlarge the results for the wide forest areas with statistical models. Paper presents a method of the TLS and ALS point cloud registration and transformation to one coordinate system. The goal of the data fusion was the semi-automatic extraction of the trees selected parameter (height, DBH, basal area, crown closure, base of crown, 2D and 3D tree crown surface) of the TR2 transect in Niepolomice Forest (Krakow, Poland). The results showed that the big potential of the enhancement of height and crown closure or base of the crown exists. Resumo O inventário florestal tradicional baseia-se normalmente em milhares de parcelas circulares (raio = 12,62 m, área 500 m²) dispostas em uma malha regular. Em cada parcela do inventário, os parâmetros básicos da árvore e do povoamento devem sercoletados usando suta e hipsômetro ou até mesmo o olho humano (densidade de copa). Os modernos modelos florestais multifuncionais requerem cada vez mais: dados densos e acurados para gerar o mais rápido possível a informação precisa da quantidade de estoque de madeira ou outro parâmetro selecionado do povoamento e da árvore. Uma das tecnologias promissoras de sensoriamento remoto é o LiDAR coletando os dados da nuvem de pontos 3D. A tecnologia TLS é muito precisa e rápida mas limitada a áreas relativamente pequenas como as parcelas de inventários florestais. A ALS é mais focada na coleta de dados em grandes áreas. Ambas as tecnologias são complementares portanto, é necessário para a fusão das duas fontes de informação aumentar a acurácia dos parâmetros de árvore e ampliar os resultados para grandes áreas florestais com modelos estatísticos. O artigo apresenta um método de registro e transformação do TLS e nuvem de pontos do ALS para um sistema de coordenadas. O objetivo da fusão dos dados foi a extração semi automática de parâmetros selecionados de árvores (altura, DAP, área basal, densidade de copa, base da copa, área 2D e 3D da copa da árvore) do transecto TR2 da Floresta Niepolomice (Krakow, Polônia). Os resultados mostraram que o grande potencial do aprimoramento da altura e do densidade de copa ou da base da copa existe

A spatially explicit database of wind disturbances in European forests over the period 2000-2018

Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000-2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30% of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land-atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND

The integration of the terrestrial and airborne laser scanning technologies in the semi-automated process of retrieving selected trees and forest stand parameters Integração das tecnologias terrestre e aerotransportada de scanner laser no processo semi-automático de recuperação de árvores selecionadas e de parâmetros de povoamentos florestais

The traditional forest inventory based usually on thousands of the circle plots (radius = 12.62m; area 500 sq m) set in regular network. On every inventory plot, the basic tree and forest stand parameters have to be collected using calliper and hypsometer or even human eye (i.e. crown closure) as well. The modern multifunctional forestry models requires more and more: dens and accurate data to deliver as fast as possible the precise information on the amount of wood stock or other selected forest stand and tree parameters. One of the promising remote sensing technologies is LiDAR collecting the 3D point cloud data. The TLS technology is very precise and fast but is limited to relatively small areas like forest inventory plot. The ALS is more focused on wide-area data collection. Both technologies are complementary, therefore it is a need for the fusion of those two sources of information to enhance the accuracy of tree parameters and enlarge the results for the wide forest areas with statistical models. Paper presents a method of the TLS and ALS point cloud registration and transformation to one coordinate system. The goal of the data fusion was the semi-automatic extraction of the trees selected parameter (height, DBH, basal area, crown closure, base of crown, 2D and 3D tree crown surface) of the TR2 transect in Niepolomice Forest (Krakow, Poland). The results showed that the big potential of the enhancement of height and crown closure or base of the crown exists.ResumoO inventário florestal tradicional baseia-se normalmente em milhares de parcelas circulares (raio = 12,62 m, área 500 m²) dispostas em uma malha regular. Em cada parcela do inventário, os parâmetros básicos da árvore e do povoamento devem sercoletados usando suta e hipsômetro ou até mesmo o olho humano (densidade de copa). Os modernos modelos florestais multifuncionais requerem cada vez mais: dados densos e acurados para gerar o mais rápido possível a informação precisa da quantidade de estoque de madeira ou outro parâmetro selecionado do povoamento e da árvore. Uma das tecnologias promissoras de sensoriamento remoto é o LiDAR coletando os dados da nuvem de pontos 3D. A tecnologia TLS é muito precisa e rápida mas limitada a áreas relativamente pequenas como as parcelas de inventários florestais. A ALS é mais focada na coleta de dados em grandes áreas. Ambas as tecnologias são complementares portanto, é necessário para a fusão das duas fontes de informação aumentar a acurácia dos parâmetros de árvore e ampliar os resultados para grandes áreas florestais com modelos estatísticos. O artigo apresenta um método de registro e transformação do TLS e nuvem de pontos do ALS para um sistema de coordenadas. O objetivo da fusão dos dados foi a extração semi automática de parâmetros selecionados de árvores (altura, DAP, área basal, densidade de copa, base da copa, área 2D e 3D da copa da árvore) do transecto TR2 da Floresta Niepolomice (Krakow, Polônia). Os resultados mostraram que o grande potencial do aprimoramento da altura e do densidade de copa ou da base da copa existe

Forest cover changes in Gorce NP (Poland) using photointerpretation of analogue photographs and GEOBIA of orthophotos and nDSM based on image-matching based approach

Forest cover change can be detected with high precision using 3D geospatial data and semi-automatic analyses of Remote Sensing data. The aim of our study, performed in Gorce National Park in Poland, was to generate a land use land cover (LULC) map and use it to analyse forest cover change. The study area is a subalpine forest region that has been affected by bark beetle and wind disturbances. The Geographic Object-Based Image Analysis approach was used for classification, with Colour Infrared orthophotos and normalized Digital Surface Models generated using image-matching approach. Gathered results showed that dominating LULC class is coniferous forests (3380 ha; 47% of study area), when second largest class is deciduous forests (2204 ha; 30%). The dead Norway spruce stands (465.5 ha; 6.5%) showed significant increase comparing to 114.1 ha mapped in 1997

Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of 3 buildings between 2001–2019 was +16 million m . The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.SusDEen

A spatially explicit database of wind disturbances in European forests over the period 2000-2018

Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have ntensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in largescale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019)JRC.D.1-Bio-econom