Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sandoval, J. S. Onésimo; Maimaitiyiming, Matthew

doi:10.1016/j.jag.2014.08.020

Cited by 96 publications

(61 citation statements)

References 60 publications

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“…We used remote sensing data to understand and quantify the rate of change occurring in the study sites. We integrated remote sensing and census data [34] based on 1-km grid cells [35] using OLS regression and local level GWR statistical methods, which offered a richer framework for understanding detailed trends of urban growth [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A Spatial Analysis of the Relationship between Vegetation and Poverty

Dawson

Sandoval

Sagan

et al. 2018

IJGI

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The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cities with a population ≥250,000 for 16 Radiometer 1-kilometer (1-km). Second, from the pixel-level regression, we selected five U.S. cities (Shrinking: Chicago, Detroit, Philadelphia, and Growing: Dallas and Tucson) that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical cities. Finally, we used spatial statistics to investigate the relationship between census tract level data (i.e., poverty, population, and race) and vegetation for 2010, based on the 1-km grid cells using Ordinary Least Squares Regression and Geographically Weighted Regression. Our results revealed poverty related areas were significantly correlated with positive high and/or negative high vegetation in both shrinking and growing cities. This paper makes a contribution to the academic body of knowledge on U.S. urban shrinking and growing cities by using a comparative analysis with global and local spatial statistics to understand the relationship between vegetation and socioeconomic inequality. neighborhoods in crisis undergo abandonment and vacancy, all of which has the positive potential to drive an increase in vegetation due to clearance and natural ecosystem changes. This study addressed the following research question. What is the relationship between poverty and vegetation? Building on previous research on vegetation, our paper makes original and significant contributions to the corpus of literature on U.S. shrinking and growing cities [3,4]. First, we used a comparative analysis to study three shrinking and two growing cities that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical city. Second, we used global and local spatial statistics to study the spatial relationship between vegetation and socioeconomic inequality. Finally, we employed a novel methodology to study this relationship by using 1-km grids, rather than U.S. census tracts. The findings from this research provide much needed insight on the difference between shrinking and growing cities.

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Section: Introductionmentioning

confidence: 99%

A Spatial Analysis of the Relationship between Vegetation and Poverty

Dawson

Sandoval

Sagan

et al. 2018

IJGI

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“…Thus, the GWR model indicated the spatial correlations between the variables by generating a series of location-specific parameters to visualize geographical interactions [54,55]. OriginPro version 9.0 (OriginLab, Northampton, MA, USA), a data analysis and graphing software, was used to graphically represent the results from an analysis of the variables.…”

Section: Statistical Analysis and Relationship Calculationsmentioning

confidence: 99%

“…Therefore, the calculated grid cells were counted for about 31,737 for the entire area. For the GWR model, as a set of parameters [55], a local R squared parameter was chosen, among others, to evaluate the model's performance. Thus, local R squared values were used to map the relationships between NDVI-Precipitation, NDVI-LST, and NDVI-ET ( Figure 6).…”

Section: Analysis Of Correlation Between Ndvi and Climatic Factors (Pmentioning

confidence: 99%

Analysis of Climate and Topography Impacts on the Spatial Distribution of Vegetation in the Virunga Volcanoes Massif of East-Central Africa

et al. 2017

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This paper aimed to investigate the influence of climatic and topographic factors on the distribution of vegetation in the Virunga Volcanoes Massif using GIS and remote sensing techniques. The climatic variables considered were precipitation, Land Surface Temperature (LST), and evapotranspiration (ET), whereas the topographic factors considered were elevation and aspect. The dataset consisted of MODIS NDVI data, satellite-delivered precipitation, ET, and the LST. A 2014 Landsat 8 OLI image was used to produce a vegetation map of the study area, while DEM was used to derive the elevation attributes and to calculate the aspect angles. Moran's I and Geographically Weighted Regression (GWR) Model was used to analyze the relationships between the climatic factors and NDVI changes over elevation and aspect. The results indicated that among the nine vegetation types inventoried in the area, the Mean NDVI varied from 0.33 to 0.59 and the optimal vegetation growth was found at an elevation between 2000 and 3900 m, with mean NDVI values larger than 0.50. The peak mean NDVI value of 0.59 was found at the elevation from 2100 to 2800 m. Vegetation growth was found to be more sensitive to elevation, as NDVI values were more varied at a lower elevation (<4000 m) than at a higher elevation (>4000 m). Considering the aspect, the greater vegetation growth was found in SE (132 • , 148 • ), SW (182 • , 186 • ), and NW (309.5 • -337.5 • ), with mean NDVI values larger than 0.56. This indicated that vegetation was susceptible to better growth conditions in the lower elevation ranges and in shady areas. The vegetation NDVI in this study area was mostly uncorrelated with precipitation (R 2 = 0.34), but was strongly correlated with LST (R 2 = 0.99) and ET (R 2 = 98). LST (≥18 • C) and ET (1286 mm/year −1 ) were found to provide optimal conditions for vegetation growth in the Virunga Volcanoes Massif. Empirically, the results concluded that elevation, aspect, LST, and ET are the main factors controlling the spatial distribution and vegetation growth in this area. This information is significantly helpful for biodiversity conservation and constitutes a valuable input to environmental and ecological research.

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“…Quantitative spatial analysis of LCLUC characteristics over time and space is of paramount significance for understanding the impacts of human activity and natural environmental change [31]. Grid cell method provides a quantitative measure of spatial-temporal dynamics of LCLUC [32,33]. It allows the researcher(s) to analyze different spatial characteristics of LCLUC within and outside BNR to further reveal anthropogenic disturbance on the ecosystems.…”

Section: Grid Cell Processesmentioning

confidence: 99%

“…In most cases, these pixels were assigned to the cell that covered the largest part of the pixel [32]. The problem with this method is that the sum of all land cover areas within one grid can exceed the area of the grid cell itself [33]. To avoid this problem, we converted each raster LCLU map to a vector map, then intersected each land cover type of LCLU vector map with 100 square meter empty grid cell map, separately.…”

Section: Grid Cell Processesmentioning

confidence: 99%

Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways

Ghulam

Maimaitijiang

et al. 2015

Remote Sensing

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Abstract:In this paper, grid cell based spatial statistics were used to quantify the drivers of land-cover and land-use change (LCLUC) and habitat degradation in a tropical rainforest in Madagascar. First, a spectral database of various land-cover and land-use information was compiled using multi-year field campaign data and photointerpretation of satellite images. Next, residential areas were extracted from IKONOS-2 and GeoEye-1 images using object oriented feature extraction (OBIA). Then, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to generate land-cover and land-use maps from 1990 to 2011, and LCLUC maps were developed with decadal intervals and converted to 100 m vector grid cells. Finally, the causal associations between LCLUC were quantified using ordinary least square regression analysis and Moran's I, and a forest disturbance index derived from the time series Landsat data were used to further confirm LCLUC drivers. The results showed that (1) local spatial statistical approaches were most effective at quantifying the OPEN ACCESS Remote Sens. 2015, 7 6258 drivers of LCLUC, and (2) the combined threats of habitat degradation in and around the reserve and increasing encroachment of invasive plant species lead to the expansion of shrubland and mixed forest within the former primary forest, which was echoed by the forest disturbance index derived from the Landsat data.

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Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

Cited by 96 publications

References 60 publications

A Spatial Analysis of the Relationship between Vegetation and Poverty

A Spatial Analysis of the Relationship between Vegetation and Poverty

Analysis of Climate and Topography Impacts on the Spatial Distribution of Vegetation in the Virunga Volcanoes Massif of East-Central Africa

Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways

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