The Response of African Land Surface Phenology to Large Scale Climate Oscillations

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa

AGRICULTURAL CHANGE IN THE RUSSIAN GRAIN BELT: A CASE STUDY OF SAMARA OBLAST

Change in agricultural land use in Samara Oblast is analyzed on the basis of agricultural statistics, field observations, and satellite imagery. Besides the general decline in animal husbandry, three drivers of spatial change are uncovered—accessibility to the major urban areas, natural setting, and ethnic mix. Land surface phenology metrics are in line with these drivers. In particular, satellite imagery confirms the large amount of fallowed land in Samara. Overall, land abandonment reached its peak in the late 1990s, and was subsequently reversed but the amount of land used in crop farming has not reached the 1990 level. Spatial differentiation is also analyzed across three types of farms—former collective and state farms, household farms, and registered family businesses

Identifying priority sites for low impact development (LID) in a mixed-use watershed

AbstractLow impact development (LID), a comprehensive land use planning and design approach with the goal of mitigating land development impacts to the environment, is increasingly being touted as an effective approach to lessen runoff and pollutant loadings to streams. Broad-scale approaches for siting LID have been developed for agricultural watersheds, but are rare for urban watersheds, largely due to greater land use complexity. Here, we introduce a spatially-explicit approach to assist landscape architects, urban planners, and water managers in identifying priority sites for LID based exclusively on freely available data. We use a large, mixed-use watershed in central Oklahoma, the United States of America, as a case-study to demonstrate our approach. Our results indicate that for one sub-catchment of the Lake Thunderbird Watershed, LID placed in 11 priority locations can facilitate reductions in nutrient and sediment loading to receiving waters by as much as 16% and 17%, respectively. We had a high rate of correctly identified sites (94±5.7%). Our systematic and transferable approach for prioritizing LID sites has the potential to facilitate effective implementation of LID to lessen the effects of urban land use on stream ecosystems

Quantifying Exceptionally Large Populations of \u3ci\u3eAcropora\u3c/i\u3e spp. Corals Off Belize Using Sub-Meter Satellite Imagery Classification

Caribbean coral reefs have experienced dramatic declines in live coral cover in recent decades. Primary branching framework Caribbean corals, Acropora cervicornis (Lamarck, 1816) and Acropora palmata (Lamarck, 1816), have suffered the greatest collapse. Coral Gardens, Belize, is one of few remaining, and perhaps the largest, refugia for abundant, healthy, but undocumented populations of both Acropora species in the Caribbean Sea. In the present study, GeoEye-1 multispectral satellite imagery of a 25 km2 reefal area near Ambergris Caye, Belize, was analyzed to identify live Acropora spp. cover. We used a supervised classification to predict occurrence of areas with live Acropora spp. and to separate them from other benthic cover types, such as sandy bottom, seagrass, and mixed massive coral species. We tested classification accuracy in the field, and new Acropora spp. patches were mapped using differential GPS. Of 11 predicted new areas of Acropora spp., eight were composed of healthy Acropora spp. An unsupervised classification of a red (Band 3):blue (Band 1) ratio calculation of the image successfully separated Acropora corals from other benthic cover, with an overall accuracy of 90%. Our study identified 7.58 ha of reef dominated by Acropora spp. at Coral Gardens, which is one of the largest populations in the Caribbean Sea. We suggest that Coral Gardens may be an important site for the study of modern Acropora spp. resilience. Our technique can be used as an efficient tool for genera-specific identification, monitoring, and conservation of populations of endangered Acropora spp

Evaluation of geoimputation strategies in a large case study

Background: Health data usually has missing or incomplete location information, which impacts the quality of research. Geoimputation methods are used by health professionals to increase the spatial resolution of address information for more accurate analyses. The objective of this study was to evaluate geo-imputation methods with respect to the demographic and spatial characteristics of the data. Methods: We evaluated four geoimputation methods for increasing spatial resolution of records with known locational information at a coarse level. In order to test and rigorously evaluate two stochastic and two deterministic strategies, we used the Texas Sex Ofender registry database with over 50,000 records with known demographic and coordinate information. We reduced the spatial resolution of each record to a census block group and attempted to recover coordinate information using the four strategies. We rigorously evaluated the results in terms of the error distance between the original coordinates and recovered coordinates by studying the results by demographic sub groups and the characteristics of the underlying geography. Results: We observed that in estimating the actual location of a case, the weighted mean method is the most superior for each demographic group followed by the maximum imputation centroid, the random point in matching sub-geographies and the random point in all sub-geographies methods. Higher accuracies were observed for minority populations because minorities tend to cluster in certain neighborhoods, which makes it easier to impute their location. Results are greatly afected by the population density of the underlying geographies. We observed high accuracies in high population density areas, which often exist within smaller census blocks, which makes the search space smaller. Similarly, mapping geoimputation accuracies in a spatially explicit manner reveals that metropolitan areas yield higher accuracy results. Conclusions: Based on gains in standard error, reduction in mean error and validation results, we conclude that characteristics of the estimated records such as the demographic profle and population density information provide a measure of certainty of geographic imputation. Keywords: Geo-imputation, Address data, Coarse resolution, Census data, DemographicsThis work was supported by The Oklahoma Center for the Advancement of Science and Technology, Grant No. HR16-048. Article processing charges funded in part by University of Oklahoma Libraries.Yes"International Journal of Health Geographics operates a single-blind peer-review system, where the reviewers are aware of the names and affiliations of the authors, but the reviewer reports provided to authors are anonymous.

Large Scale Climate Oscillation Impacts on Temperature, Precipitation and Land Surface Phenology in Central Asia

Central Asia has been rapidly changing in multiple ways over the past few decades. Increases in temperature and likely decreases in precipitation in Central Asia as the result of global climate change are making one of the most arid regions in the world even more susceptible to large-scale droughts. Global climate oscillations, such as the El Ni ̃no–Southern Oscillation, have previously been linked to observed weather patterns in Central Asia. However, until now it has been unclear how the different climate oscillations act simultaneously to affect the weather and subsequently the vegetated land surface in Central Asia.We fit well-established land surface phenology models to two versions of MODIS data to identify the land surface phenology of Central Asia between 2001 and 2016. We then combine five climate oscillation indices into one regression model and identify the relative importance of each of these indices on precipitation, temperature, and land surface phenology, to learn where each climate index has the strongest influence. Our analyses illustrate that the North Atlantic Oscillation, the East Atlantic/West Russia pattern, and the AtlanticMulti-Decadal Oscillation predominantly influence temperature in the northern part of Central Asia.We also show that the Scandinavia index and the Multivariate ENSO index both reveal significant impacts on the precipitation in this region. Thus, we conclude that the land surface phenology across Central Asia is affected by several climate modes, both those that are strongly linked to far northern weather patterns and those that are forced by southern weather patterns, making this region a \u27climate change hotspot’ with strong spatial variations in weather patterns.We also show that regional climate patterns play a significant role in Central Asia, indicating that global climate patterns alone might not be sufficient to project weather patterns and subsequent land surface changes in this region

Northern Eurasia Future Initiative (NEFI): Facing the Challenges and Pathways of Global Change in the Twenty-first Century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies codesigned with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

A statistical framework for the analysis of long image time series: The effect of anthropogenic change on land surface phenology

Significant global changes affect the carbon and water cycles as well as the biodiversity on earth. Mapping and monitoring these changes can aid in the understanding and distinction between anthropogenic and biophysical impacts on the land surface. In the context of scientific and social debate on the pace and extent of global climate change, it is extremely important to have methods that are capable of distinguishing between expected variability and significant change. In this dissertation I have presented a statistical framework for the analysis of long image time series that consists of robust techniques for step change analysis, temporal trend analysis, and the modeling of land surface phenology (LSP) and analysis of LSP change. This framework helps to fill a gap in the remote sensing literature on appropriate approaches to quantitative change analysis. I have described two main application areas for the statistical framework: (1) Quality analysis of NOAA AVHRR NDVI datasets. The analysis of more than 2 million km2 of desert and semi-desert ecoregions in Central Asia revealed significant sensor artifacts in the Pathfinder AVHRR Land (PAL) NDVI dataset. I have found that the comparison of data from any combination of NOAA-7, NOAA-9 and NOAA-14 can be used for land surface change analyses, but that the inclusion of NOAH-11 AVHRR NDVI data in trend analyses may result in the detection of spurious trends. Furthermore, I have shown that two versions of NOAA AVHRR NDVI datasets with similar characteristics can yield very different conclusions on land surface change. (2) Using the PAL NDVI data, I applied the framework to address the question of whether the institutional changes accompanying the collapse of the Soviet Union resulted in significant changes in land surface phenologies across Northern Eurasia and Kazakhstan in particular. Using multiple lines of evidence provided by the statistical framework, I was able to distinguish between anthropogenic impacts and interannual climatic fluctuations on the land surface phenology