Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture.

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.

Temporal changes in giant panda habitat connectivity across boundaries of Wolong Nature Reserve, China.

Global biodiversity loss is largely driven by human activities such as the conversion of natural to human-dominated landscapes. A popular approach to mitigating land cover change is the designation of protected areas (e.g., nature reserves). Nature reserves are traditionally perceived as strongholds of biodiversity conservation. However, many reserves are affected by land cover changes not only within their boundaries, but also in their surrounding areas. This study analyzed the changes in habitat for the giant panda (Ailuropoda melanoleuca) inside Wolong Nature Reserve, Sichuan, China, and in a 3-km buffer area outside its boundaries, through a time series of classified satellite imagery and field observations. Habitat connectivity between the inside and the outside of the reserve diminished between 1965 and 2001 because panda habitat was steadily lost both inside and outside the reserve. However, habitat connectivity slightly increased between 1997 and 2001 due to the stabilization of some panda habitat inside and outside the reserve. This stabilization most likely occurred as a response to changes in socioeconomic activities (e.g., shifts from agricultural to nonagricultural economies). Recently implemented government policies could further mitigate the impacts of land cover change on panda habitat. The results suggest that Wolong Nature Reserve, and perhaps other nature reserves in other parts of the world, cannot be managed as an isolated entity because habitat connectivity declines with land cover changes outside the reserve even if the area inside the reserve is well protected. The findings and approaches presented in this paper may also have important implications for the management of other nature reserves across the world.

Interactive effects of natural and human disturbances on vegetation dynamics across landscapes.

Accurate measures of human effects on landscape processes require consideration of both the direct impacts from human activities and the indirect consequences of the interactions between humans and the landscape. This is particularly evident in systems experiencing regular natural disturbances such as in the mountainous areas of southwestern China, where the remaining population of giant pandas (Ailuropoda melanoleuca) is supported. Here the spatiotemporal patterns of human impacts, forests, and bamboo episodic die-offs combine to determine the distribution of panda habitat. To study the complex interactions of humans and landscapes, we developed an integrated spatiotemporally explicit model of household activities, natural vegetation dynamics, and their impacts on panda habitat. Using this model we examined the direct consequences of local fuelwood collection and household creation on areas of critical giant panda habitat and the indirect impacts when coupled with vegetation dynamics. Through simulations, we found that over the next 30 years household impacts would result in the loss of up to 30% of the habitat relied on by pandas during past bamboo die-offs. The accumulation and spatial distribution of household impacts would also have a considerable indirect influence on the spatial distribution of understory bamboo. While human impacts influence both bamboo die-off and regeneration, over 19% of pre-existing low-elevation bamboo habitat may be lost following an episodic die-off depending on the severity of the impacts and timing of the die-offs. Our study showed not only the importance of the spatial distribution of direct household impacts on habitat, but also the far-reaching effects of the indirect interactions between humans and the landscapes they are modifying.

Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions.

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.