The increasing global trend in reported disasters and economic damage shows that the most adversely affected is the Asia-Pacific region, especially developing countries like the Philippines. The Philippines is located in the Circum-Pacific Ring of Fire, a volcanically and seismically active zone. It lies in the Western North Pacific Basin, where tropical cyclogenesis is most active. While volcanic eruptions and earthquakes have long recurrence intervals (ranging from decades to centuries), rainfall induced landslides (RIL) and the damage they cause are dealt with almost every month of the rainy season. Locally and globally, the northern Philippines is among the most landslide prone and is among the most adversely affected.
In order to foster community resilience, more landslide-related science-based information over space and time is essential. This study investigated the timing and impact of RIL, the amount of 24-hour and antecedent rainfall associated with RIL, and the weather conditions that enhance landslide triggering rainfall (LTR). The mountainous region of the Baguio district, where the highest 24-hour rainfall has been recorded, was chosen as the area of study. A threshold for LTR as basis for early warning was then established. The results showed that early warning for landslides may be based on one or a combination of the following: (1) 24-hour rainfall of 70 mm, (2) intensity (I) – duration (D) equation: I = 6.46 D -0.28, (3) normalized ID equation: NI = 0.002 D -0.28, (4) 24-hour rainfall that is 0.02%-28% of the mean annual precipitation, and (5) antecedent rainfall of 500 mm over a 60-day period. During a tropical cyclone event, the knowledge of accumulated rainfall can provide immediate and real-time information to signal needed action, e.g. mobilization of emergency crews, road closure, work suspension and evacuation of those at highest risk.
The study constructed a landslide inventory from high resolution satellite imagery (HRSI), field observations and local knowledge in the southern area of the municipality of Tublay in Benguet province, some 20 kilometers north of Baguio city. Utilizing remote sensing and GIS software, a semi-automated method combined with a manual method was adopted to highlight 853 landslides, most of which were slides and debris flows.
With available satellite imagery and access to remote sensing, GIS and statistical software, robust estimates of landslide susceptibility were generated in a process that is expeditious, straightforward, evidence-based and cost-effective. A methodology for estimating attributes of selected landslide-conditioning factors and modelling landslide susceptibility was developed. The bivariate and multivariate statistical methods of frequency ratio and binary logistic regression, respectively, were applied. A five-fold cross-validation approach in the application of the frequency ratio method demonstrated that the five factors most closely associated with RIL were NDVI < 0.38, slope is 50-60 degrees, elevation is 1800-2000 meters, aspect is south and distance to drainage is >500 m. The landslide susceptibility models that were generated using DEM, scanned maps, and HRSI factor sets separately and in combination showed consistent results. The combination of the HRSI factor set with the DEM or scanned map factor sets improved model performance significantly. The landslide susceptibility models using all factor sets provided the best results. The average success and prediction rates were 90% and 89%, respectively.
The effect of training and validation data size was investigated in the application of the binary logistic regression. The training–validation proportions were 80%-20%, 60%-40%, 40%-60% and 20%-80%. Ten sample data sets in each proportion group were examined. Based on the coefficients obtained, the factors NDVI, LULC, aspect, lithology and slope showed strong influence on landslide occurrence. The factors plan curvature, distance to fault/lineament and distance to road were also important contributors to the models generated. Training and validation accuracy, ranging 89%-95% and 84%-95%, respectively, were best obtained using the 80%-20% data proportion. Validation accuracy diminished with decreasing training data size.
The use of cross-validation and multiple training and validation data sets confirmed the model consistency and generated robust results. They are thus advocated in future assessments of landslide susceptibility. The landslide susceptibility maps could serve as reference for identifying no-build, high maintenance and safe zones, complementing information from the available landslide hazard maps (1:50000 and 1:10000) of national government agencies and community stakeholders. The methodology and data products could be refined and replicated in similar landslide-prone regions. It is hoped that the findings reported would contribute to ongoing efforts towards building a more landslide disaster resilient community.

The Sundarbans mangrove forest is an important resource for the people of the Ganges Delta. It plays an important role in the local as well as global ecosystem by providing ecological services and economic goods. However, this mangrove ecosystem is under threat, mainly due to climate change and anthropogenic factors. The aims of this Thesis are: (1) to apply remote sensing techniques and open source mid-resolution data, as cheap and reliable data, to identify and map mangrove composition at species level, (2) to monitor the impact of climate variability in this ecosystem , (3) assess spatial and temporal dynamics of tidal channels in the Bangladesh Sundarbans and finally (4) to predict the magnitude of mangrove area loss and future impacts on mangrove species composition and distribution due to a rise in Mean Sea Level (MSL
Chapter one evaluates the efficacy of mid-resolution Landsat satellite image combined with traditional classification algorithms to produce an acceptable accuracy at species level mapping of mangroves. A maximum likelihood algorithm was employed to identify and map mangrove species composition using open-source mid-resolution Landsat data, taking Bangladesh Sundarbans as a case study. The classified image achieved an overall accuracy of 89.10% and kappa coefficient of 0.87 for the five-identified species, viz. Heritiera fomes, Ceriops decandra, Excoecaria agallocha, Sonneratia apelatala, and Xylocarpus mekongensis, which is higher than the required minimum overall accuracy of 85% deemed suitable to use in most of the natural resource mapping applications. Based on our result, it can be concluded that mid-resolution images, such as Landsat, and the traditional classification algorithm can be applied with confidence for the identification and classification of mangrove forest resources at species level as an alternative to the high resolution satellite images.
The second research chapter is about mapping the long term changes in mangrove species composition in the Sundarbans. Maximum likelihood classifier technique was employed to classify images recorded by the Landsat satellite series and used post classification comparison techniques to detect changes at the species level. The image classification resulted in overall accuracies of 72%, 83%, 79% and 89% for the images of 1977, 1989, 2000 and 2015, respectively. We identified five major mangrove species and detected changes over the 38-year (1977–2015) study period. During this period, both Heritiera fomes and Excoecaria agallocha decreased by 9.9%, while Ceriops decandra, Sonneratia apelatala, and Xylocarpus mekongensis increased by 12.9%, 380.4% and 57.3%, respectively.
The third research chapter presents the relationship between temperature, rainfall pattern and dynamics of mangrove species in the Sundarbans, Bangladesh, assessed over a 38 year time period from 1977–2015. A three stage analytical process was employed to monitor the impact of climate variability in this ecosystem. Primarily, the trend of temperature and rainfall over the study period were identified using a linear trend model; then, the supervised maximum likelihood classifier technique was employed to classify images recorded by Landsat series and postclassification comparison techniques were used to detect changes at species level. The rate of change of different mangrove species was also estimated in the second stage. Finally, the relationship between temperature, rainfall and the dynamics of mangroves at species level was determined using a simple linear regression model. A significant statistical relationship between temperature, rainfall and the dynamics of mangrove species was obtained. The trends of change for Heritiera fomes and Sonneratia apelatala show a strong relationship with temperature and rainfall, while Ceriops decandra shows a weak relationship. In contrast, Excoecaria agallocha and Xylocarpus mekongensis do not show any significant relationship with temperature and rainfall. This chapter concluded that temperature and rainfall are important climatic factors influencing the dynamics of three major mangrove species viz. Heritiera fomes, Sonneratia apelatala and Ceriops decandra in the Sundarbans.
The fourth research chapter focuses on the spatial and temporal dynamics of tidal channels in the Bangladesh Sundarbans. Parts of the Passur River system were considered for this investigation. Tidal channel bank layers were extracted from aerial photographs from 1974 and 2011, and a Sentinel-2 image from 2017. Remote Sensing and Geographic Information System (GIS) platforms were used to analyse, interpret, and visualize data on accretion and erosion, as well as the locations of the tidal channel bank over different years. The results revealed that erosion was severe in the larger channels, whereas accretion was dominant in the smaller channels. Displacement of the tidal channel bank has had a profound impact on the Sundarbans mangrove ecosystem, and continued erosion and accretion processes are of concern for the future sustainability of biodiversity in the Sundarbans. While in the short term these changes may not have much impact, over decades the dynamics of tidal channels may significantly contribute to the imbalance of fauna and flora in the Sundarbans.
A synthesis of the forcing mechanisms of tidal channel dynamics in the context of natural and anthropogenic forces and their implications on the Sundarbans delta floodplain mangrove forest comes in the fifth research chapter. Natural tidal channel dynamics driving forces viz: tectonic and subsidence, sea level rise, tides, storms, cyclones and other climatic factors are discussed in this synthesis. Human induced morphodynamic factors that affect erosion and accretion processes in the Sundarbans tidal channel system are also discussed. Based on our discussion it can be concluded that natural and anthropogenic forces such as tides, storms and cyclones, fluctuations in seasonal rainfall, tectonic and subsidence forces, sea level rise, infrastructure development and changing pattern of land use plays a vital role in the erosion accretion processes in tidal channel dynamics in the study area, and subsequently have important implications on the sustainability of the Sundarbans mangrove ecosystem. Precise effects of these natural and anthropogenic forces are recommended for future research.
The sixth research chapter predicts the magnitude of mangrove area loss and future impacts on mangrove species composition and distribution due to a rise in Mean Sea Level (MSL). In this study, a geospatial model of potentially inundated areas was developed using Digital Elevation Model (DEM) data to assess the potential impacts of sea level rise (SLR) on the future spatial distribution of mangrove species and estimates the potential inundation and subsequent mangrove area loss. The mangrove areas of 2646 ha, 9599 ha and 74720 ha are projected to be inundated and subsequently lost by the end of the 21st century for the low, medium and high SLR scenarios respectively under the net subsidence rate -2.4 mm/year relative to the baseline year 2000. All the major five mangrove species of the Bangladesh Sundarbans will be affected and that can potentially contribute to a change in the present species composition and biodiversity of the forest. Results suggest that, under the extreme scenario, inundation and subsequent loss of different mangrove species will be substantial and this can bring a massive change in the species composition and their spatial distribution in the Bangladesh Sundarbans.
In conclusion, long term changes in mangrove cover at species level, and prediction of future spatial distributions under different climate change scenarios in the Bangladesh Sundarbans are mapped and analysed in this thesis. In addition, a newly developed geospatial model shows the future impact of sea level rise on species composition and their spatial distribution. This model can be used in future for the impact assessment of sea level rise on mangrove species in the Sundarbans and other parts of the world. Thus, this research provides some invaluable insights and techniques for the development of a proper monitoring strategies in the future for sustainable management of the forest in response to climate variability and change. Different relevant agencies of Bangladesh government, such as Bangladesh Forest Department and Ministry of Environment, can follow the approach and incorporate the outcomes of this research to develop a continuous and proper monitoring system in a time saving, efficient and cost effective way. Hope outcome of this study will be a stepping stone in the studies of the Sundarbans mangroves and its sustainable management using remote sensing techniques.

The quantification, mapping and monitoring of biomass are now central issues due to the importance of biomass as a renewable energy source in many countries of the world. The estimation of biomass is a challenging task, especially in areas with complex stands and varying environmental conditions, and requires accurate and consistent measurement methods. To efficiently and effectively use biomass as a renewable energy source, it is important to have detailed knowledge of its distribution, abundance, and quality. Remote sensing offers the technology to enable rapid assessment of biomass over large areas relatively quickly and at a low cost. This paper provides a comprehensive review of biomass assessment techniques using remote sensing in different environments and using different sensing techniques. It covers forests, savannah, and grasslands/rangelands, and for each of these environments, reviews key work that has been undertaken and compares the techniques that have been the most successful.

Information on wetland condition can be used for various decision-making processes for better management of this vital resource. Salt marshes are complex ecosystems that are not well mapped and understood. This research was conducted to assess the potential of high-spatial and high-spectral resolution satellite data to map and monitor salt-marsh vegetation communities of Micalo Island of New South Wales, Australia. The aim of the study was to determine whether different salt-marsh vegetation species could be differentiated using high-spectral and high-spatial resolution imagery and whether these could be linked to wetland condition. To compare sensor capabilities in discriminating salt-marsh vegetation, high-spatial data sets from Quickbird and highspectral data sets from Hyperion were used. A hybrid unsupervised and supervised classification procedure was used to assess the wetland mapping potential of the Quickbird and Hyperion data. The supervised classification results had greater overall and within-class accuracies and showed greater promise. Most of the vegetation species were identified and mapped correctly. One area of concern was the misclassification of 'Sporobolus' into grass categories while using Quickbird imagery, mainly where the 'Sporobolus' was tall and dry. They look very similar to the tall reedy grass. The mapping results can be useful in establishing baseline information for subsequent studies involving change detection of salt-marsh ecosystems.