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

The use of computer graphics and virtual reality for visual impact assessments

Changes to the visual character of the landscape can become a key issue capable of influencing the outcome of an Environmental Impact Assessment (EIA). These changes are commonly referred to as visual impact, and it is recognised, unlike many other aspects of EIAs, that Visual Impact Assessment (VIA) relies less upon measurement than upon experience and judgement (IEATLI, 1995). Currently, there are very few quantitative techniques for the evaluation of visibility and it is mostly assessed qualitatively since it is concerned with the human appreciation of the landscape (Zewe and Koglin, 1995 and Wherrett, 2002). The main problem with qualitative techniques in assessing visual impact is that they may lead to bias due to their inherent subjectivity, hence there is a need for a more structured and consistent approach towards VIA. To reduce the subjectivity currently associated with VIAs, new quantitative techniques have been developed and existing spatial and qualitative techniques have been improved upon. The techniques developed in this research use Computer Graphics (CG) technology, including the field of Virtual Reality (VR). A quantitative method to calculate percentage view change has been developed that allows the accurate determination of the variation in any view, caused by an existing or proposed development. The method uses three dimensional (3D) CG models of an environment and software that has been developed using a scripting language from a 3D modelling software package. A new method has also been developed to create Fields of Visual Influence (FVIs) using standard 3D modelling techniques. The method improves upon the accuracy and efficiency of existing FVI techniques. A novel VR simulation technique has also been developed that attempts to reduce the subjectivity associated with simulations, by integrating quantitative and spatial techniques

Envisioning 3D learning environments in environmental education: an exploration of the Konza Prairie

Master of Landscape ArchitectureLandscape Architecture/Regional and Community PlanningHoward D. Hahn“There is an alarming gap between awareness and action on [environmental issues]” (Sheppard, 2005). Public awareness of how to cope and change with these issues is lacking (Sheppard, 2004; Nicholson-Cole, 2005; Dockerty et al., 2005), but new visualization technologies can begin to bridge the gap through environmental education. Environmental education focuses on the user exploring an environment, environmental issues, problem solving and ways to mitigate these issues. While the younger generations (middle to high school students) are much more aware of current and future environmental issues than older generations, the solutions to these problems may not be so apparent. By combining the need to educate young adults about climate change, regional ecosystem climate mitigation, and ecological management for technologically driven youth, middle and high school students can better understand their environment’s impact on climate-change regulation. Through literature synthesis, documentation of existing visualization exhibits and technologies, and preliminary technology exploration, a production process, criteria, framework, and technology recommendations were established. These components informed the final storyboards, which visually organized a proposal to build a 3D learning environment focused on the Konza Prairie and its ecological management practices

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Extraction of Urban Environmental Quality Indicators using LiDAR-Based Digital Surface Models

The visualization of specific 3-D urban scenes can be done calling upon different techniques, from those more traditional, such as photogrammetry, to the most advanced ones, such as laser scanning that uses different techniques and algorithms of selection and modelling of 3-D point clouds. The use and utility of this kind of data for the study of urban development remain however debatable. Indeed, indicators for urban development and durability are highly necessary and the best methodology to build them is largely open. This thesis anticipates the use of 2-D and 3-D models and data for the environmental analysis of cities, aiming to provide useful tools for urban planning and design. According to end-users requirements, the extraction of urban environmental quality (UEQ) indicators from 2-D and 3-D information using innovative methods is proposed and implemented, which is based on recent research on computational algorithms for the analysis, evaluation, management and design of the urban space. Moreover, results that can be obtained with different data sources and aggregation methods are compared. In particular, the main advantages of urban models generated from LiDAR data are highlighted. In consequence, an iterative process is proposed, involving professionals of various fields, aiming at improving the utility of those indicators for the support of applied decision activities related to the sustainable development of cities. This process is sub-divided in three correlated steps: A preliminary inquiry concerning the user requirements for the implementation of a 3-D project of the State/City of Geneva was launched. Based on the obtained replies, several potential applications related to both the definition and extraction of urban indicators were identified, and also, end-users were classified into 6 different domains: 1– architecture, urbanism and territory planning; 2– urban traffic (motor vehicles, trains and airplanes); 3– environment and energy; 4– pedestrian and cyclist mobility; 5– security and emergency situations management; 6– underground information; Based on point 1. and according to the assessment of the specific needs among each of these domains, several interviews were carried out in which 25 end-users decided to focus on UEQ indicators considering three main stakes: 1– assessment of the morphological properties of the urban texture; 2– exploration of the solar potential on the urban fabric; 3– estimation of the energy demand on the urban fabric. Many empirical case-studies are emphasized, mostly for the city of Geneva, and also for the cities of Lausanne and Florence. These indicators are extracted from the segmentation of planar roof areas using classified LiDAR point clouds and the use of image processing techniques based on Digital Elevation Models (DEM) and Digital Height Models (DHM), defined in this thesis as 2.5-Digital Urban Surface Models (2.5-DUSM) and normalized 2.5-Digital Urban Surface Models (n2.5-DUSM) respectively. These models are constructed in a step by step basis, using LiDAR and 2-D and 3-D vector data, thus applying different methods of interpolation and enhancement, whose accuracy is also evaluated on a statistical basis; Finally, an inquiry on how the same group of 25 end-users mentioned in point 1. perceives and interprets the different exploratory 2-D and 3-D geo-visualizations proposed for some of the UEQ indicators is undertaken, evaluating their utility according to the requirements previously defined

Evaluation of low-cost Earth observations to scale-up national forest monitoring in Miombo Woodlands of Malawi

This study explored the extent that low-cost Earth Observations (EO) data could effectively be combined with in-situ tree-level measurements to support national estimates of Above Ground Biomass (AGB) and Carbon (C) in Malawi’s Miombo Woodlands. The specific objectives were to; (i) investigate the effectiveness of low-cost optical UAV orthomosaics in geo-locating individual trees and estimating AGB and C, (ii) scale-up the AGB estimates using the canopy height model derived from the UAV imagery, and crown diameter measurements; and (iii) compare results from (ii), ALOS-PALSAR-2, Sentinel1, ESA CCI Biomass Map datasets, and Sentinel 2 vis/NIR/SWIR band combination datasets in mapping biomass. Data were acquired in 2019 from 13 plots over Ntchisi Forest in 3-fold, vis-a-vis; (i) individual tree measurements from 0.1ha ground-based (gb) plots, (ii) 3-7cm pixel resolution optical airborne imagery from 50ha plots, and (iii) SAR backscatter and Vis/NIR/SWIR bands imagery. Results demonstrate a strong correlational relationship (R2 = 0.7, RMSE = 11tCha-1) between gb AGB and gb fractional cover percent (FC %), more importantly (R2 = 0.7) between gb AGB and UAV-based FC. Similarly, another set of high correlation (R2 = 0.9, RMSE = 7tCha-1; R2 = 0.8, RMSE = 8tCha-1; and R2 = 0.7) was observed between the gb AGB and EO-based AGB from; (i) ALOS-PALSAR-2, (ii) ESA-CCI-Biomass Map, and (iii) S1-C-band, respectively. Under the measurement conditions, these findings reveal that; (i) FC is more indicative of AGB and C pattern than CHM, (ii) the UAV can collect optical data of very high resolution (3-7cm resolution with ±13m horizontal geolocation error), and (iii) provides the cost-effective means of bridging the ground datasets to the wall-to-wall satellite EO data (£7 ha-1 compared to £30 ha-1, per person, provided by the gb system). The overall better performance of the SAR backscatter (R2 = 0.7 to 0.9) establishes the suitability of the SAR backscatter to infer the Miombo AGB and fractional cover with high accuracy. However, the following factors compromised the accuracy for both the SAR and optical measurements; leaf-off and seasonality (fire, aridness), topography (steep slopes of 18-74%), and sensing angle. Inversely, the weak to moderate correlation observed between the gb height and UAV FC % measurements (R2 = 0.4 to 0.7) are attributable to the underestimation systematic error that UAV height datasets are associated with. The visual lacunarity analysis on S2-Vis/NIR/SWIR composite band and SAR backscatter measurements demonstrated robust, consistent and homogenous spatial crown patterns exhibited particularly by the leaf-on tree canopies along riverine tree belts and cohorts. These results reveal the potential of vis/NIR/SWIR band combination in determining the effect of fire, rock outcrops and bare land/soil common in these woodlands. Coarsening the EO imagery to ≥50m pixel resolution compromised the accuracy of the estimations, hence <50m resolution is the ideal scale for these Miombo. Careful consideration of the aforementioned factors and incorporation of FC parameter in during estimation of AGB and C will go a long way in not only enhancing the accuracy of the measurements, but also in bolstering Malawi’s NFMS standards to yield carbon off-set payments under the global REDD+ mechanism

Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material

This book focuses on the use of GIScience in conjunction with historical visual sources to resolve past scenarios. The themes, knowledge gained and methodologies conducted might be of interest to a variety of scholars from the social science and humanities disciplines

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