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.

Models of change processes created with the Markov chain model (MCM) can be used in the interpolation of temporal data and in short-term change projections. However, there are two major issues associated with the use of Markov models for land-cover change projections: the stationarity of change and the impact of neighboring cells on the change areas. This study addressed these two issues using an investigation of five time-series land-cover datasets generated between 1972 and 2009 for the Liverpool region of NSW, Australia. Four short term transition matrices were computed, and the results were used to predict land-cover distributions for the near future. The issue of neighborhood effects was addressed by incorporating spatial components in a Cellular Automata (CA)-based MCM, and the results were compared with those derived from a normal MCM. Given the marginal improvements in the simulation achieved with CA-MCM rather than MCM, and because of the ability of CA-MCM to incorporate spatial variants, CA-MCM was determined to be the more suitable method for predicting land-cover changes for the year 2019. The land-cover projection indicated that future land-cover changes will likely continue to affect the natural vegetation, which will in turn likely decrease through the continued conversion of natural to agricultural lands over the years.

While there are many definitions of rangeland, the central theme of all these is that it is land on which the dominating vegetation is mainly grasses, grass-like plants, forbs, shrubs, and isolated trees. Rangelands include shrublands, natural grasslands, woodlands, savannahs, tundra, and many desert regions. A distinguishing factor of rangelands from pasture lands is that they grow primarily native vegetation, rather than plants established by humans. Rangelands are also managed mainly through extensive practices such as managed livestock grazing and prescribed fire rather than more intensive agricultural practices and the use of fertilizers. Rangelands worldwide are known to provide a wide range of desirable goods and services, including but not limited to livestock forage, wildlife habitat, wood products, mineral resources, water, and recreation space. Large populations depend on rangelands for their livelihoods, hence effective monitoring and management is crucial for sustainable production, health, and biodiversity of these systems.

Various anthropogenic transformations and modifications have continuously modified and/or changed the land cover for centuries for different forms of human productions. These ultimately impacted or changed the biodiversity, nutrient and hydrological cycles as well as global environment and climate, especially in the developing world. Australia has a great variety of native vegetation ranging from rainforests, alpine habitats, wetlands, grasslands, eucalypt forests and woodlands reflecting the diversity of species, habitats and ecosystems found across the country. The destruction of habitat due to native vegetation clearing has been identified as the greatest single threat to biodiversity in New South Wales (NSW), Australia. The clearing has mainly taken place in grassy woodlands areas for pasture improvement by the application of fertilizers, ploughing and the sowing of introduced grasses and clovers. This research explored the potential of a range of remote sensing and modelling techniques to assist in the investigation of suitability of land cover mapping in terms of time-period, methods, and seasonal and long term land cover change in the north-eastern parts of NSW, Australia. The overall aim of this research was to investigate the potential of remote sensing, GIS and modelling techniques in detailed investigations of seasonal and nearly four decadal land cover change analysis and assessment of long term pattern of land cover change for future change predictions. The research also aimed at assessing the impact of land cover change on terrestrial habitat configurations.

Urban built-up area information is required in various applications of land use planning and management. However, urban built-up area extraction from moderate spatial resolution Landsat time-series data is challenging because of significant intra-urban heterogeneity and spectral confusion between other landcover types. This paper proposes a technique to extract urban built-up area from time-series Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imageries and determines urban area changes between 1984 to 2015 of Adama Municipal Area of Ethiopia. The study selected three indices, the Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI) and Modified Normalized Difference Water Index (MNDWI), to represent three major urban land-use classes: built-up and barren/bare land, open waterbody, and vegetation, respectively. The built-up area was extracted by taking the difference between EBBI, SAVI and MNDWI to remove the vegetation and water noises, and the resulted index image was spectrally segmented to separate built-up area from the non-urban built-up lands. The derived index was used to map built-up area for 1984, 1995, 2005 and 2015 periods. The expansion of the built-up area has been revealed as a major change in the area when city area expanded substantially by 293% between 1984 to 2015 periods. The advantage of the method was to use almost the entire spectral range of Landsat imageries which cause less spectral confusion between land cover classes and hence resulted in higher accuracies compared to other indices. The method was effective and simple to implement, and can be used for built-up extraction in other areas.

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.

Binary images from one or more spectral bands have been used in many studies for land-cover change/ no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy 'Independent Two-Step' thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ~ 3% in overall accuracy and of ~0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis.

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.