This review consists of a systematic assessment of climate change adaptation literature to elicit major trends, discourses, and patterns in how local knowledge is conceived. We report on conceptual and geographic trends within the literature, including the practice of assessing local knowledge against scientific benchmarks, and present results of a textual network analysis that illustrates overlap and co‐occurrence among different characterizations of local knowledge. In critically assessing the dominant trends we draw special attention to problems associated with the extraction of local knowledge without due consideration of how this process is embedded and inextricable from local contexts and sociotechnical orders. Drawing on theories of science and technology that examine the ontological politics of research practices, we propose a co‐productive path forward for local knowledge mobilization to inform adaptation decision‐making, which we argue facilitates the transformation of the institutional and governance arrangement of climate adaptation to provide greater flexibility and experimentalism in research and decision‐making. WIREs Clim Change 2017, 8:e475. doi: 10.1002/wcc.475 This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice

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Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of -benchmarks‖; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques' abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change.

This research extends upon land cover change studies by incorporating methodological approaches, which are compatible with heterogeneous ecosystems, are able to link landscape changes to system processes, such as climate change, and provide potential linkages to concepts of ecological resilience. The study region in southern Africa experienced a significant climatic shift in the 1970s, resulting in drier conditions. The state of these ecosystems and their response to such climatic shock is quantified in terms of vegetation amount and heterogeneity. We monitor these characteristics pre- and post-disturbance using a Landsat image series and examine the utility of continuous characterizations of land cover for measuring ecosystem resilience. Land cover change is evaluated using a mean-variance analysis in concert with a spatial persistence analysis. This investigation indicates that although the impact of the decreased precipitation is evident in the 1980s, recovery occurred by the 1990s and 2000s. We found the continuous methodological approach used holds potential for studying heterogeneous landscapes within a resilience framework

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.