Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Improving the estimation of fire danger, fire propagation and fire monitoring : new insights using remote sensing data and statistical methods

This thesis covers three major topics related to wildfires, remote sensing and meteorology: (i) quantifying and forecasting fire danger combining numerical weather forecasts and satellite observations of fire intensity; (ii) mapping burned areas from satellite observations with multiple spatial and spectral resolution; and (iii) modelling fire progression taking into account weather conditions and fuel (vegetation) availability. Regarding the first topic, an enhanced Fire Weather Index (FWI) is proposed by using statistical methods to combine the classical FWI with an atmospheric instability index with the aim of better forecasting the fire danger conditions favourable to the development of convective fires. Furthermore, the daily definition of the classical FWI was extended to an hourly timescale, allowing for assessment of the variability of the fire danger conditions throughout the day. For the second topic, a method is proposed to map and date burned areas using sequences of daily satellite data. This method, tested over several regions around the globe, provide burned area maps that outperform other existing methods for the task, particularly regarding the consistency and accuracy of the date of burning. Furthermore, a method is proposed for fast assessment of burned areas using 10-meter resolution satellite data and making use of Google Earth Engine (GEE) as a tool for preprocessing and downloading of data that is then used as input to a deep learning model that combines a coarse burned area map with the medium resolution data to provide a refined burned area map with 10-meter resolution at event level and with low computational requirements. Finally, for the third topic, a method is proposed to estimate the fire progression over a 12-hour period with resource to an ensemble of models trained based on the reconstruction of past events. Overall, I am confident that the results obtained and presented in this thesis provide a significant contribution to the remote sensing and wildfires scientific community while opening interesting paths for future research on the topics described

D6.6: 7 conference papers

The Deliverable 6.6 with the title “7 conference papers”, is part of WP6 “Dissemination and Exploitation” of Athena project with a basic aim to knowledge sharing, network development and exposure to an international environment. Three conference attendances were foreseen (e.g. CAA; SPIE; EARSeL) within the project duration whereas more than 30 posters and oral presentations were presented during the project in the conferences such as: SPIE 2016, SPIE 2018, EUROMED 2016, EUROMED 2018, EGU 2016, EGU 2017, EGU 2018, RSCy2016, RSCy 2017, RSCy 2018, etc

Remote Sensing and GIS Applications in Wildfires

Wildfires are closely associated with human activities and global climate change, but they also affect human health, safety, and the eco-environment. The ability of understanding wildfire dynamics is important for managing the effects of wildfires on infrastructures and natural environments. Geospatial technologies (remote sensing and GIS) provide a means to study wildfires at multiple temporal and spatial scales using an efficient and quantitative method. This chapter presents an overview of the applications of geospatial technologies in wildfire management. Applications related to pre-fire conditions management (fire hazard mapping, fire risk mapping, fuel mapping), monitoring fire conditions (fire detection, detection of hot-spots, fire thermal parameters, etc.) and post-fire condition management (burnt area mapping, burn severity, soil erosion assessments, post-fire vegetation recovery assessments and monitoring) are discussed. Emphasis is given to the roles of multispectral sensors, lidar and evolving UAV/drone technologies in mapping, processing, combining and monitoring various environmental characteristics related to wildfires. Current and previous researches are presented, and future research trends are discussed. It is wildly accepted that geospatial technologies provide a low-cost, multi-temporal means for conducting local, regional and global-scale wildfire research, and assessments

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

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

Remote Sensing of Rapid Permafrost Landscape Dynamics

The global climate is warming and the northern high latitudes are affected particularly rapidly. Large areas of this region, or 24% of the northern hemisphere, are influenced by perennially frozen ground or permafrost. As permafrost is predominantly dependent on cold mean annual air temperatures, climate warming threatens the stability of permafrost. Since large amounts of organic carbon are stored within permafrost, its thaw would potentially release large amounts of greenhouse gases, which would further enhance climate warming (permafrost carbon feedback). Thermokarst and thermo-erosion are an indicator of rapid permafrost thaw, and may also trigger further disturbances in their vicinity. The vast Arctic permafrost regions and the wide distribution of thaw landforms makes the monitoring of thermokarst and thermo-erosion an important task to better understand the response of permafrost to the changing climate. Remote sensing is a key methodology to monitor the land surface from local to global spatial scales and could provide a tool to quantify such changes in permafrost regions. With the opening of satellite archives, advances in computational processing capacities and new data processing technology, it has become possible to handle and analyze rapidly growing amounts of data. In the scope of the changing climate and its influence of permafrost in conjunction with recent advances in remote sensing this thesis aims to answer the following key research questions: 1. How can the extensive Landsat data archive be used effectively for detecting typical land surface changes processes in permafrost landscapes? 2. What is the spatial distribution of lake dynamics in permafrost and which are the dominant underlying influencing factors? 3. How are key disturbances in permafrost landscapes (lake changes, thaw slumps and fire) spatially distributed and what are their primary influence factors? To answer these questions, I developed a scalable methodology to detect and analyze permafrost landscape changes in the ~29,000 km2 Lena Delta in North-East Siberia. I used all available peak summer data from the Landsat archive from 1999 through 2014 and applied a highly automated robust trend-analysis based on multi-spectral indices using the Theil-Sen algorithm. With the trends of surface properties, such as albedo, vegetation status or wetness, I was able identify local scale processes, such as thermokarst lake expansion and drainage, river bank erosion, and coastal inundation, as well as regional surface changes, such as wetting and greening at 30m spatial resolution. This method proved to be robust in indicating typical landscape change processes within an Arctic coastal lowland environment dominated by permafrost, which has been challenging for the application of optical remote sensing data. The scalability of the highly automated processing allows for further upscaling and advanced automated landscape process analysis. For a targeted analysis of well-known disturbances affecting permafrost (thermokarst lakes, retrogressive thaw slumps and wildfires), I used advanced remote sensing and image processing techniques in conjunction with the processed trend data. Here I combined the trend analysis with machine-learning classification and object based image analysis to detect lakes and to quantify their dynamics over a period from 1999 through 2014 within four different Arctic and Subarctic regions in Alaska and Siberia totaling 200,000 km². I found very strong precipitation driven lake expansion (+48.48 %) in the central Yakutian study area, while the study areas along the Arctic coast showed a slight loss of lake area (Alaska North Slope: -0.69%; Kolyma Lowland: -0.51%) or a moderate lake loss (Alaska Kobuk-Selawik Lowlands: -2.82%) due to widespread lake drainage. The lake change dynamics were characterized by a large variety of local dynamics, which are dependent on several factors, such as ground-ice conditions, surface geology, or climatic conditions. In an even broader analysis across four extensive north-south transects covering more than 2.3 million km², I focused on the spatial distribution and key factors of permafrost region disturbances. I found clear spatial patterns for the abundance of lakes (predominantly in ice-rich lowland areas), retrogressive thaw slumps (predominantly in ice-rich, sloped terrain, former glacial margin), and wildfires (boreal forest). Interestingly, apart from frequent drainage at the continuous-discontinuous permafrost interface, lake change dynamics showed spatial patterns of expansion and reduction that could not be directly related to specific variables, such as climate or permafrost conditions over large continental-scale transects. However, specific variables could get related to specific lake dynamics in within locally defined regions. Trend datasets of vegetation status (NDVI) were combined with high-resolution detailed geomorphological land-cover classification information and climate data to map tundra productivity in a heterogeneous landscape in northern Alaska. After decades of increasing productivity (greening), recently tundra vegetation showed a reverse trend of decreased productivity, which is predicted to continue with increasing temperatures and precipitation. In this thesis project I developed methods to analyze rapid landscape change processes of various scales in northern high latitudes with unprecedented detail by relying on spatially and temporally high resolution Landsat image time series analysis across very large regions. The findings allow a unique and unprecedented insight into the landscape dynamics of permafrost over large regions, even detecting rapid permafrost thaw processes, which have a small spatial footprint and thus are difficult to detect. The multi-scaled approach can help to support local-scale field campaigns to precisely prepare study site selection for expeditions, but also pan-arctic to global-scale models to improve predictions of permafrost thaw feedbacks and soil carbon emissions in a warming climate

Integrating Remote Sensing Techniques into Forest Monitoring: Selected Topics with a Focus on Thermal Remote Sensing

A sustainable management of natural resources, in particular of forests, is of great importance to preserve the ecological, environmental and economic benefits of forests for future generations. An enhanced understanding of the current situation and ongoing trends of forests, e.g. through policy interventions, is crucial to managing the forest wisely. In this context, forest monitoring is essential for collecting the base data required and for observing trends. Despite the wide range of approved methods and techniques for both close-range and satellite-based remote sensing monitoring, ongoing forest monitoring research is still grappling with specific and unresolved questions: The data acquired must be more reliable, in particular over a long-term period; costs need to be reduced through advancements in both methods and technology that offer easier and more feasible ways of interpreting data. This thesis comprises a number of focused studies, each with their individual and specific research questions, and aims to explore the benefits of innovative methods and technologies. The main emphasis of the studies presented is the integration of close-range and satellite-based remote sensing for enhancing the efficiency of forest monitoring. Manuscript I discusses thermal canopy photography, a new field of application. This approach takes advantage of the large differences in temperature between sky and non-sky pixels and overcomes the inconsistencies of finding an optimal threshold. For an unambiguously separation of “sky” and “non-sky” pixels, a global threshold of 0 °C was defined. Currently, optical or hemispherical canopy photography is the most widely used method to extract crown-related variables. However, a number of aspects, such as exposure, illumination conditions, and threshold definition present a challenge in optical canopy photography and dramatically influence the result; consequently, a comparison of the results from optical canopy photography at a different point in time derived is not advisable. For forest monitoring, where repeated measurements of the canopy cover on the same plots were undertaken, it is therefore of utmost importance to devise a standard protocol to estimate changes in and compare the canopy covers. This paper offers such a protocol by introducing thermal canopy photography. A feasible and accurate method that examines the strong correlation (R2 = 0.96) of canopy closure values derived from thermal and optical image pairs. Thermal photography, as a close-range remote sensing technique, also aids data collection and analysis in other contexts, for instance to expand our knowledge about bamboo tree species: Information about the maturity of bamboo culms is of utmost importance for managing bamboo stands because only then the process of lignification is finished and the culm is technically stronger and more resistant to insect and fungi attacks. The findings of a study (Manuscript III) conducted in Pereira, Colombia, show small differences in culm surface temperature between culms of different ages for the bamboo species Guadua angustifolia K., which may be a sign of maturity. The surface temperature of 12 culms was measured after sunrise using the thermal camera system FLIR 60Ebx. This study shows an innovative close-range remote sensing technique which may support researchers’ determination of the maturity of bamboo culms. This research is in its inception phase and our results are the first of this kind. In the context of analyzing, in particular of thermal imagery time-series data, Manuscript (IV) offers a new methodology using advanced statistical methods. Otsu Thresholding, an automatic segmentation technique is used in a first processing step. O’Sullivan penalized splines estimated the temperature profile extracted from the canopy leaf temperature. A final comparison of the different profiles is done by constructing simultaneous confidence bands. The result shows an approximately significant difference in canopy leaf temperature. For this study, we successfully cooperated with the Center for Statistics at Göttingen University (Prof. Kneib). The second close-range remote sensing technology employed in this thesis is terrestrial laser scanning which is used here to enhance our understanding about buttressed trees. Big trees with an irregular non-convex shape are important contributors to aboveground biomass in tropical forests, but an accurate estimation of their biomass is still a challenge and often remains biased. Allometric equations including tree diameter and height as predictors are currently used in tropical forests, but they are often not calibrated for such large and irregular trees where measuring the diameter is quite difficult. Against this background, Manuscript II shows the result of the 3D-analysis of 12 buttressed trees. This study was conducted in the Botanical Garden of Bogor, Indonesia, using a state-of-the-art terrestrial laser scanner. The findings allow for new insights into the irregular geometry of buttressed trees and the methodological approach employed in this paper will help to improve volume and biomass models for this kind of tree. The results suggest a strong relationship (R² = 0.87) between cross-sectional areas at diameter above buttress (DAB) height and the actual tree basal area measured at 1.3 m height. The accuracy of field biomass estimates is crucial if the data are used to calibrate models to predict the forest biomass on landscape level using remote sensing imagery. The linkage between technology and methodology in the context of forest monitoring remote sensing enhance our knowledge in extracting more reliable information on tree cover estimation. The pre-processing of satellite images plays a crucial role in the processing workflow and particularly the illumination correction has a direct effect on the estimated tree cover. Manuscript IV evaluates four DEMs (Pleiades DSM, SRTM30, SRTM V4.1 and SRTM-X) that are available for the area of Shitai County (Anhui Province, Southeast China) for the purpose of an optimized illumination correction and tree cover estimation from optical RapidEye satellite images. The findings presented in this study suggest that the change in tree cover is contingent on the respective digital elevation models used for pre-processing the data. Imagery corrected with the freely available SRTM30 DEM with 30 m resolution leads to a higher accuracy in the estimation of tree cover based on the high-resolution and cost intensive Pleaides DEM. These manuscripts eventually seek to resolve some of the issues and provide answers to some of the detailed questions that still persist at different steps of the forest monitoring process. In future, these new and innovate methods and technologies will maybe integrate into forest monitoring programs