Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.

In Thailand, pig production intensified significantly during the last decade, with many economic, epidemiological and environmental implications. Strategies toward more sustainable future developments are currently investigated, and these could be informed by a detailed assessment of the main trends in the pig sector, and on how different production systems are geographically distributed. This study had two main objectives. First, we aimed to describe the main trends and geographic patterns of pig production systems in Thailand in terms of pig type (native, breeding, and fattening pigs), farm scales (smallholder and large-scale farming systems) and type of farming systems (farrow-to-finish, nursery, and finishing systems) based on a very detailed 2010 census. Second, we aimed to study the statistical spatial association between these different types of pig farming distribution and a set of spatial variables describing access to feed and markets. Over the last decades, pig population gradually increased, with a continuously increasing number of pigs per holder, suggesting a continuing intensification of the sector. The different pig-production systems showed very contrasted geographical distributions. The spatial distribution of large-scale pig farms corresponds with that of commercial pig breeds, and spatial analysis conducted using Random Forest distribution models indicated that these were concentrated in lowland urban or peri-urban areas, close to means of transportation, facilitating supply to major markets such as provincial capitals and the Bangkok Metropolitan region. Conversely the smallholders were distributed throughout the country, with higher densities located in highland, remote, and rural areas, where they supply local rural markets. A limitation of the study was that pig farming systems were defined from the number of animals per farm, resulting in their possible misclassification, but this should have a limited impact on the main patterns revealed by the analysis. The very contrasted distribution of different pig production systems present opportunities for future regionalization of pig production. More specifically, the detailed geographical analysis of the different production systems will be used to spatially-inform planning decisions for pig farming accounting for the specific health, environment and economical implications of the different pig production systems.

The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.

Precipitation variability at inter- and intra-annual scales may influence land-use management decisions in semi-arid savannas worldwide, and in particular, over the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa. Over KAZA, spatiotemporal precipitation variability forced by the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is important to local and regional-scale decisions for planting, livestock grazing, and wildlife migration patterns. We investigate the annual water year (October–September) and early rainy season [October–November–December (OND)] precipitation patterns during 60 years before and after a 1970s shift in the global ocean–atmosphere system for this region in southern Africa. The coincidence of the number of wet (upper tercile) and dry (lower tercile) years and OND seasons during the various phases of ENSO and IOD are compared prior to (1950–1975) and after (1980–2008) the 1970s climate shift over KAZA. Since the 1970s climate shift, KAZA has been significantly drier and observed fewer wet years and OND seasons. ENSO is the dominant forcing of precipitation differences over southern Africa before and after the 1970s climate shift, with IOD playing a secondary role. However, when ENSO and IOD phases are analysed simultaneously, El Niño and positive IOD events are significantly related to dry periods. The forcing of atmospheric circulation over southern Africa before and after the 1970s climate shift during El Niño and La Niña events is significantly different. Prior to the 1970s climate shift, atmospheric circulation during El Niño (La Niña) favoured strong (weak) precipitation increases (decreases). Afterward the 1970s climate shift, atmospheric circulation during La Niña (El Niño) favoured strong (weak) precipitation decreases (increases). The shifting nature of climate modes, especially ENSO, and respective influence on rainfall variability for southern Africa is important to understand to better inform seasonal climate forecasts to improve operational decision-making for land-use and water management decisions in semi-arid savanna regions.

Understanding how intra-annual precipitation variability affects seasonal vegetation dynamics is critical for assessing the potential impacts of climate variability on vegetation structure and composition. This is important in semi-arid and arid ecosystems of southern Africa, where water is a limiting resource and timing of seasonal rainfall combined with the water storage capacity of different plant vegetation types affects remotely measured phenological cycles. Various lags and leads of savanna vegetation response to rainfall have been identified using remotely sensed data, but little attention has been given to vegetation greenness leading into the dry season. Vegetation production at the onset of the dry season interacts with the availability of water resources affecting fire dynamics, forage materials for wildlife and wildlife movement throughout the dry season. This is important for southern Africa as large proportions of the human population and economy are dependent on wildlife tourism. This study investigates the response of the end of the wet season vegetation production, as measured by Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) greenness, to the different months of wet season rainfall for different savanna vegetation types in a regional catchment of southern Africa. We estimated monthly precipitation using Tropical Rainfall Monitoring Mission 3B43 data and used MODIS 13A1 NDVI at a monthly time step for the years 2000–2009. Our model estimated greenness at the beginning of the dry season from the prior rainy season (October–April) precipitation using geographically weighted regression (GWR). The results show intra-annual wet season rainfall accounts for significant amounts of April NDVI variation. Overall intra-annual rainfall has a stronger effect for areas with greater proportions of grassland and dry, deciduous woodlands than for wetter, evergreen woodlands. These findings support previous research by highlighting the stronger association of grassland and open canopy woodlands to the end of the wet season monthly rainfall. This relationship is important for understanding seasonal precipitation effects on different savanna vegetation covers.

Reliable mapping of tree cover and tree-cover change at regional, continental, and global scales is critical for understanding key aspects of ecosystem structure and function. In savannas, which are characterized by a variable mixture of trees and grasses, mapping tree cover can be especially challenging due to the highly heterogeneous nature of these ecosystems. Our objective in this article was to develop improved tools for large-scale classification of savanna tree cover in grass-dominated savanna ecosystems that vary substantially in woody cover over fine spatial scales. We used multispectral, low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to identify the bands and metrics that are best suited to quantify woody cover in an area of the Serengeti National Park, Tanzania. We first used 1-m resolution panchromatic IKONOS data to quantify tree cover for February 2010 in an area of highly variable tree cover. We then upscaled the classification to MODIS (250 m) resolution. We used a 2 year time series (IKONOS date ± 1 year) of MODIS 16 day composites to identify suitable metrics for quantifying tree cover at low resolution, and calculated and compared the explanatory power of three different variable classes for four MODIS bands using Lasso regression: longitudinal summary statistics for individual spectral bands (e.g. mean and standard deviation), Fourier harmonics, and normalized difference vegetation index (NDVI) green-up metrics. Longitudinal summary statistics showed better explanatory power (R ² = 73% for calibration data; R ² = 61% for validation data) than Fourier or green-up metrics. The mid-infrared, near-infrared, and NDVI bands were all important predictors of tree cover. Mean values for the time series were more important than other metrics, suggesting that multispectral data may be more valuable than within-band seasonal variation obtained from time series data for mapping tree cover. Our best model improved substantially over the MODIS Vegetation Continuous Fields product, often used for quantifying tree cover in savanna systems. Quantifying tree cover at coarse spatial resolution using remote-sensing approaches is challenging due to the low amount and high heterogeneity of tree cover in many savanna systems, and our results suggest that products that work well at global scales may be inadequate for low-tree-cover systems such as the Serengeti. We show here that, even in situations where tree cover is low (<10%) and varies considerably across space, satisfactory predictive power is possible when broad spectral data can be obtained even at coarse spatial resolution.