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

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

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

Regional MODIS Analysis of Abandoned Agricultural Lands in the Kazakh Steppes

The institutional consequences of the Soviet Union’s collapse in 1991 have been greatly underestimated as a significant forcing on the boundary layer through changes in land surface phenology. Upon independence, Kazakhstan lost the centralized agricultural planning, crop subsidy system, and access to international markets that the Soviet Union had been providing. These institutional changes led to substantial decreases in livestock populations, especially sheep, and in arable land area cultivated, especially rain-fed spring wheat in northern Kazakhstan. As a result the fallow fields reverted to weedy species and idle pastures produced denser grass cover. In this case study we used MODIS imagery from three consecutive growing seasons (2001-2003) to study the processes that occur after arable land has been abandoned, a process that is often underestimated in carbon cycle modeling, but which is especially important in central and northern Eurasia. We selected two study regions in northern Kazakhstan: one in the Kazakh forest steppe ecoregion and another in the Kazakh steppe ecoregion. Within each region we visually determined a spring wheat area, and an area with other land cover. We then constructed land surface phenology models based on MODIS imagery from March 2001 through October 2003 and accumulated growing degree-days (AGDD) calculated from the NCEP/NCAR Reanalysis data. Comparisons among these phenology models provide insight into the land cover change trajectories that have occurred since large areas of arable land have gone fallow. Furthermore, we determine the change in parameters based on different land cover percentages. We have shown that abandoned arable lands green up before croplands that are plowed for planting in the last week of May. This may explain, in part, the “greening trend” that has been observed over northern Eurasia by other investigations

Land Surface Dynamics in Kazakhstan: Dynamic Baselines and Change Detection

Analysis of the spatio-temporal structure of NDVI in the Pathfinder AVHRR Land data set for Kazakhstan from 1981-1999 reveals significant changes in the distributions of the scale of fluctuation (SOF) before and after 1992 in some ecoregions at certain phases of the growing season. These differences are likely due to actual influences on the land surface and not changes in sensor characteristics. Further analysis is required to identify and quantify these influences

Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm

The objective of this paper is to demonstrate a new method to map the distributions of C3 and C4 grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C3 and C4 grasses in our study area such as elevation, soil pH, soil organic matter and soil texture

Surface water detection in the Caucasus

The Caucasus is an important global diversity hotspot and hosts a wide variety of surface water features, including major transboundary wetlands, in addition to large areas with irrigated agriculture and newly developed fishponds. In this study, we aim to establish the best performing methodology to produce surface water maps with a high degree of accuracy in the Caucasus. We evaluate optical data from Landsat 8 in both the dry and wet season for three study areas in the Caucasus. We test the performance of four different optical water indices derived from Landsat data, a method by Zou et al. (2017) also applied to Landsat data, and the European Commission Joint Research Centre (ECJRC) Global Surface Water dataset. We evaluate the performance of each water index using 5744 land cover validation/training points over all three study areas, which we manually classified by evaluating imagery from Google Earth. Using all validation points from all three study areas and both the wet and dry season, we find that the application of a logistic regression model using an optical surface water index (MNDWI) resulted in the most accurate open surface water maps. This approach achieved an overall accuracy of 93.0%, which is better than was found for freely available global surface water products