scholarly journals The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland

2020 ◽  
Vol 77 (4) ◽  
Author(s):  
Ranjith Gopalakrishnan ◽  
Petteri Packalen ◽  
Veli-Pekka Ikonen ◽  
Janne Räty ◽  
Ari Venäläinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Wind Damage ◽  
Sensing Data ◽  
Wind Speeds ◽  
Airborne Laser ◽  
Forested Areas ◽  
Risk Maps ◽  
Damage Risk

Abstract Key message The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.

Modelling of a construction pit

2021 ◽  
pp. 144-149
Author(s):  
G. G. Bickbulatova ◽  
E. N. Kupreeva
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Digital Model ◽  
Geodetic Measurement ◽  
Sensing Data

There are various programs for processing geodetic measurement and remote sensing data. This article discusses the use of Cyclone software for building a digital model of a construction pit surface based on a point cloud based on laser scanning and calculating the volume of earthworks.

scholarly journals Integrated Archaeological Research: Archival Resources, Surveys, Geophysical Prospection and Excavation Approach at an Execution and Burial Site: The German Nazi Labour Camp in Treblinka

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 336
Author(s):  
Sebastian Różycki ◽  
Rafał Zapłata ◽  
Jerzy Karczewski ◽  
Andrzej Ossowski ◽  
Jacek Tomczyk
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Archaeological Research ◽  
Burial Site ◽  
Sensing Data ◽  
Non Invasive ◽  
Geophysical Surveys ◽  
Forced Labour ◽  
Labour Camp

This article presents the results of multidisciplinary research undertaken in 2016–2019 at the German Nazi Treblinka I Forced Labour Camp. Housing 20,000 prisoners, Treblinka I was established in 1941 as a part of a network of objects such as forced labour camps, resettlement camps and prison camps that were established in the territory of occupied Poland from September 1939. This paper describes archaeological research conducted in particular on the execution site and burial site—the area where the “death pits” have been found—in the so-called Las Maliszewski (Maliszewa Forest). In this area (poorly documented) exhumation work was conducted only until 1947, so the location of these graves is only approximately known. The research was resumed at the beginning of the 21st century using, e.g., non-invasive methods and remote-sensing data. The leading aim of this article is to describe the comprehensive research strategy, with a particular stress on non-invasive geophysical surveys. The integrated archaeological research presented in this paper includes an analysis of archive materials (aerial photos, witness accounts, maps, plans, and sketches), contemporary data resources (orthophotomaps, airborne laser scanning-ALS data), field work (verification of potential objects, ground penetrating radar-GPR surveys, excavations), and the integration, analysis and interpretation of all these datasets using a GIS platform. The results of the presented study included the identification of the burial zone within the Maliszewa Forest area, including six previously unknown graves, creation of a new database, and expansion of the Historical-GIS-Treblinka. Obtained results indicate that the integration and analyses within the GIS environment of various types of remote-sensing data and geophysical measurements significantly contribute to archaeological research and increase the chances to discover previously unknown “graves” from the time when the labour camp Treblinka I functioned.

TECHNOLOGY OF INTEGRATED ASSESSMENT OF WOOD RESOURCES BY REMOTE SENSING METHODS OF THE EARTH

2021 ◽  
pp. 129-138
Author(s):  
V. K. KHLYUSTOV ◽  
◽  
S. A. YURCHUK ◽  
D. V. KHLYUSTOV ◽  
A. M. GANIKHIN
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aerial Photography ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Economic Sectors ◽  
Sensing Data ◽  
The Earth ◽  
Photogrammetric Method

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.

From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data - a brief history of management inventories in the Nordic countries

2020 ◽  
Author(s):  
Matti Maltamo ◽  
Petteri Packalen ◽  
Annika Kangas
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Nordic Countries ◽  
Optical Remote Sensing ◽  
Mixed Stands ◽  
Forest Management Planning ◽  
Sensing Data ◽  
Visual Assessments

Forest Management Inventories (FMIs) provide critical information, usually at the stand level, for forest management planning. A typical FMI includes i) the delineation of the inventory area to stands by applying auxiliary information, ii) the classification of the stands according to categorical attributes, such as age, site fertility, main tree species, stand development, and iii) measurement, modelling and prediction of stand attributes of interest. The emergence of wall-to-wall remote-sensing data has enabled a paradigm change in FMIs from highly subjective, visual assessments to objective, model-based inferences. Previously, optical remote-sensing data were used to complement visual assessments, especially in stand delineation and height measurements. The evolution of airborne laser scanning (ALS) has made objective estimation of forest characteristics with known accuracy possible. New optical and Lidar-based sensors and platforms will allow further improvements of accuracy. However, there are still bottlenecks related to species-specific stand attribute information in mixed stands and assessments of tree quality. Here we concentrate on approaches and methods that have been applied in the Nordic countries in particular.

scholarly journals Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1504
Author(s):  
Sylwia Szporak-Wasilewska ◽  
Hubert Piórkowski ◽  
Wojciech Ciężkowski ◽  
Filip Jarzombkowski ◽  
Łukasz Sławik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Airborne Laser Scanning ◽  
Recursive Feature Elimination ◽  
Spectral Indices ◽  
Distribution Maps ◽  
Airborne Laser ◽  
Wetland Habitats

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.

ANALYSIS OF REMOTE SENSING DATA FOR DETERMINATION OF SPATIAL CHANGES IN ORCHARDS

2020 ◽  
Author(s):  
Vita Celmina ◽  
Vivita Pukite
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Nutrient Content ◽  
Future Research ◽  
Surface Model ◽  
Tree Crown ◽  
Sensing Data ◽  
Spatial Changes

Aim of the paper is to explore the application possibilities of remote sensing data for determination of spatial changes in orchards from 1995 to 2019. In Latvia, many fruit-growing companies have been established around the turn of the century and today have established a solid production base. Although many farms achieve good yields, the average level of productivity in orchards is insufficient. Often the yields are different in the same garden in different places. Remote sensing technology provides tree crown size data. Evaluating garden data would identify sectors with lower increments. When you see specific sectors on the map, they will be surveyed by gardeners looking for factors that have influenced tree growth (soil nutrient content, moisture content, abundant fruit yield, etc.). As a result, average productivity may increase by at least 10%, but in the longer term (5-6 years) by 20-30% Using Latvian Geospatial Information Agency’s available orthophoto and digital surface model (DSM) data, were examined three land units - orchards, where the spatial changes could be observed. The spatial changes can be observed over a longer period of time, therefore there were compared several orthophoto maps, each taken in different period of time. This study is an initial analysis of the data to determine the spatial changes. Future research will further investigate orchards with aerial laser scanning to determine accurate tree crown volumes and develop digital surface models.

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