Estimation of canopy structure and individual trees from laser scanning data

During the last fifteen years, laser scanning has emerged as a data source for forest inventory. Airborne laser scanning (ALS) provides 3D data, which may be used in an automated analysis chain to estimate vegetation properties for large areas. Terrestrial laser scanning (TLS) data are highly accurate 3D ground-based measurements, which may be used for detailed 3D modeling of vegetation elements. The objective of this thesis is to further develop methods to estimate forest information from laser scanning data. The aims are to estimate lists of individual trees from ALS data with accuracy comparable to area-based methods, to collect detailed field reference data using TLS, and to estimate canopy structure from ALS data. The studies were carried out in boreal and hemi-boreal forests in Sweden. Tree crowns were delineated in three dimensions with a model-based clustering approach. The model-based clustering identified more trees than delineation of a surface model, especially for small trees below the dominant tree layer. However, it also resulted in more erroneously delineated tree crowns. Individual trees were estimated with statistical methods from ALS data based on field-measured trees to obtain unbiased results at area level. The accuracy of the estimates was similar for delineation of a surface model (stem density root mean square error or RMSE 32.0%, bias 1.9%; stem volume RMSE 29.7%, bias 3.8%) as for model-based clustering (stem density RMSE 33.3%, bias 1.1%; stem volume RMSE 22.0%, bias 2.5%). Tree positions and stem diameters were estimated from TLS data with an automated method. Stem attributes were then estimated from ALS data trained with trees found from TLS data. The accuracy (diameter at breast height or DBH RMSE 15.4%; stem volume RMSE 34.0%) was almost the same as when trees from a manual field inventory were used as training data (DBH RMSE 15.1%; stem volume RMSE 34.5%). Canopy structure was estimated from discrete return and waveform ALS data. New models were developed based on the Beer-Lambert law to relate canopy volume to the fraction of laser light reaching the ground. Waveform ALS data (canopy volume RMSE 27.6%) described canopy structure better than discrete return ALS data (canopy volume RMSE 36.5%). The methods may be used to estimate canopy structure for large areas

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Fire models and methods to map fuel types: The role of remote sensing.

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information andmethods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

NEW, MULTI-SCALE APPROACHES TO CHARACTERIZE PATTERNS IN VEGETATION, FUELS, AND WILDFIRE

Pattern and scale are key to understanding ecological processes. My dissertation research aims for novel quantification of vegetation, fuel, and wildfire patterns at multiple scales and to leverage these data for insights into fire processes. Core to this motivation is the 3-dimensional (3-D) characterization of forest properties from light detection and ranging (LiDAR) and structure-from-motion (SfM) photogrammetry. Analytical methods for extracting useable information currently lag the ability to collect such 3-D data. The chapters that follow focus on this limitation blending interests in machine learning and data science, remote sensing, wildland fuels (vegetation), and wildfire. In Chapter 2, forest canopy structure is characterized from multiple landscapes using LiDAR data and a novel data-driven framework to identify and compare structural classes. Motivations for this chapter include the desire to systematically assess forest structure from landscape to global scales and increase the utility of data collected by government agencies for landscape restoration planning. Chapter 3 endeavors to link 3-D canopy fuels attributes to conventional optical remote sensing data with the goal of extending the reach of laser measurements to the entire western US while exploring geographic differences in LiDAR-Landsat relationships. Development of predictive models and resulting datasets increase accuracy and spatial variation over currently used canopy fuel datasets. Chapters 4 and 5 characterize fire and fuel variability using unmanned aerial systems (UAS) and quantify trends in the influence of fuel patterns on fire processes

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Earth resources: A continuing bibliography with indexes (issue 58)

This bibliography lists 500 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1988. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis