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.

Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent variability in multi-temporal data. This study builds on this important consideration and proposes a rank-based metric selection process through computation of four difference-based indices (β, γ, ε, and θ) using a Max-Min/Max normalization approach. Land cover classification was carried out for two contrasting provinces, the Liverpool Range (LR) and Liverpool Plains (LP), of the Brigalow Belt South Bioregion (BBSB) of NSW, Australia. Landsat images, Multi Spectral Scanner (MSS) of 1972-1973 and TM of 1987-1988, 1993-1994, 1999-2000 and 2009-2010 were classified using object-based image analysis methods. A total of 30 landscape metrics were computed and their sensitivities towards variation in spatial and temporal resolutions, number of land cover classes and dominant land cover categories were evaluated by computing a score based on Max-Min/Max normalization. The landscape metrics selected on the basis of the proposed methods (Diversity index (MSIDI), Area weighted mean patch fractal dimension (SHAPE_AM), Mean core area (CORE_MN), Total edge (TE), No. of patches (NP), Contagion index (CONTAG), Mean nearest neighbor index (ENN_MN) and Mean patch fractal dimension (FRAC_MN)) were successful and effective in identifying changes over five different change periods. Major changes in land cover pattern after 1993 were observed, and though the trends were similar in both cases, the LP region became more fragmented than the LR. The proposed method was straightforward to apply, and can deal with multiple metrics when selection of an appropriate set can become difficult.

This study analyses the expansion of urban growth and land cover changes in five Saudi Arabian cities (Riyadh, Jeddah, Makkah, Al-Taif and the Eastern Area) using Landsat images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out using object-based image analysis (OBIA) to create land cover maps. The classified images were used to predict the land cover changes and urban growth for 2024 and 2034. The simulation model integrated the Markov chain (MC) and Cellular Automata (CA) modelling methods and the simulated maps were compared and validated to the reference maps. The simulation results indicated high accuracy of the MC-CA integrated models. The total agreement between the simulated and the reference maps was >92% for all the simulation years. The results indicated that all five cities showed a massive urban growth between 1985 and 2014 and the predicted results showed that urban expansion is likely to continue going for 2024 and 2034 periods. The transition probabilities of land cover, such as vegetation and water, are most likely to be urban areas, first through conversion to bare soil and then to urban land use. Integrating of time-series satellite images and the MC-CA models provides a better understanding of the past, current and future patterns of land cover changes and urban growth in this region. Simulation of urban growth will help planners to develop sustainable expansion policies that may reduce the future environmental impacts.

For successful characterization of ecological processes and prioritization of habitat networks it is necessary to describe and quantify landscape structure and connectivity. However, at landscape scale, it is highly impractical to measure and map all elements of biodiversity, and therefore, biodiversity surrogates are commonly used to represent biodiversity values. Land cover and vegetation are most often used as a biodiversity surrogate. The study investigated how land use change affects the status of the biodiversity surrogates in terms of the loss or gain of habitat (areal extent), loss of habitat condition (degradation) and habitat fragmentation. Effective habitat area (EHA) and raster based cost-benefit analysis (CBA) modeling techniques were used for the assessment of the impact of land use change scenarios on wildlife habitat as biodiversity surrogates. The modeling was carried out on time-series land cover data from 1972 to 2009 for the Liverpool Range of New South Wales (NSW). The model estimated the future condition of vegetation in each and every grid-cell in the region as a function of current condition, existing land cover, and the threatening processes. The results indicated a continuous pattern of clearing in the region, while the habitat conditions were mostly static throughout the study period. There was a decline in EHA after 1993, by 3%. Clearing was identified as the main cause of such decline during the change period.

The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram-Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram-Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram-Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.

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.