Gary R. Watmough

A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+

The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.

An operational framework for object-based land use classification of heterogeneous rural landscapes

Abstract The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic landscapes. It does this by using an image stack of RGB-NIR, Normalised Difference Vegetation Index (NDVI) and textural bands in a two-phase object-based classification. The framework can be applied to data acquired by different sensors, with different view and illumination geometries, at different times of the year. Even with these complex input data the framework can produce classification results that are comparable across time. Here we describe the framework and present an example of its application using data from QuickBird (2 images) and GeoEye (1 image) sensors.

Community-Level Environmental and Climate Change Adaptation Initiatives in Nawalparasi, Nepal

Nepal is one of the poorest countries in the world and much of its rural population is at, or near, subsistence level. In recent years the timing and intensity of the monsoon in Nepal, as well as temperature extremities, have changed and this is severely impacting upon agriculture, the mainstay for over 80% of the population. Flash flooding and drought has led to landslides, water shortages and irrigation problems, which have adversely affected subsistence farming. This research conducted social surveys in rural locations to ascertain which adaptation initiatives have been implemented at the community level and determine how indigenous populations have adapted to climate-induced environmental change, with a focus on water resources. The principle research aim was to qualitatively understand how rural inhabitants have adapted/are adapting to changes in climate, the environment and water from a bottom-up perspective. Water is an essential resource for sustaining community livelihoods in rural Nepal, providing an indispensable resource for irrigation, consumption and sanitation. Research conducted in communities within the Nawalparasi district found disparities in living standards relative to resource availability. Results indicated that water stress is impacting on food security and there is a need to better adapt crop production and irrigation systems to ensure viable future sustainability. In addition, illiteracy, education facilities and accessibility were found to be strongly linked to community adaptability.

An integrated framework for assessing coastal community vulnerability across cultures, oceans and scales.

Coastal communities are some of the most at-risk populations with respect to climate change impacts. It is therefore important to determine the vulnerability of such communities to co-develop viable adaptation options. Global efforts to address this issue include international scientific projects, such as Global Learning for Local Solutions (GULLS), which focuses on five fast warming regions of the southern hemisphere and aims to provide an understanding of the local scale processes influencing community vulnerability that can then be up-scaled to regional, country and global levels. This paper describes the development of a new social and ecological vulnerability framework which integrates exposure, sensitivity and adaptive capacity with the social livelihoods and food security approaches. It also measures community flexibility to understand better the adaptive capacity of different levels of community organization. The translation of the conceptual framework to an implementable method is described and its application in a number of “hotspot” countries, where ocean waters are warming faster than the rest of the world, is presented. Opportunities for cross-cultural comparisons to uncover similarities and differences in vulnerability and adaptation patterns among the study’s coastal communities, which can provide accelerated learning mechanisms to other coastal regions, are highlighted. The social and ecological framework and the associated survey approach allow for future integration of local-level vulnerability data with ecological and oceanographic models.

Exploring the links between census and environment using remotely sensed satellite sensor imagery

Relationships are often found between socio-economic variables and environmental factors for relatively small study regions. This research forms an exploratory data analysis using logistic regression to explore the (non-causal) relationships between socio-economic variables from a national census (female literacy and involvement in economic alternatives to agricultural work) and environmental metrics extracted from Earth observation (EO) data. The relationships observed often supported those found in the literature and field observations. The research highlighted the limited but potentially valuable use of EO data for monitoring socio-economic conditions which may be used to target development assistance in the future.