Spatial causal effects on water quality are essential in identification of vulnerable watersheds. Modelling landuse variables is an effective method of projecting localized impairment. This study presents an integrate...Spatial causal effects on water quality are essential in identification of vulnerable watersheds. Modelling landuse variables is an effective method of projecting localized impairment. This study presents an integrated index, designed to gauge the ability of an 8-digit Hydrologic Unit Code watershed in its ability to produce clean water. This index, I<sub>APCW</sub>, can be successfully applied on a geospatial platform. In this study we utilized I<sub>APCW</sub> to address forest cover dynamics of an impaired watershed, that is, Missouri Watershed James Sub-region in North Dakota. Specific parametric functions were analysed and combined within a customized GIS interface to provide a multi-faceted structured technique to derive I<sub>APCW</sub>. These included ambient forest cover, housing density, agricultural land, soil erodibility and road density;it can be lucidly ascertained that where a prevailing forest cover undergoes conversion processes, the secondary effect may spur an exponential increase in water treatment costs. These parameters when projected statistically validated temporal and spatial relations of landuse/land cover dynamics to nutrient concentrations especially those that would be noted at the mouth of the watershed. In this study, we found that the levels of Total Dissolved Solids (TDS) were much higher for the years 2014 to 2016 with a discernible increased alkalizing effect within the watershed. When I<sub>APCW</sub> was compared to Annualized Agricultural Nonpoint Source (AnnAGNPS), the spatial distribution generated by the AnnAGNPS study showed that fallow areas produced significant amounts of sediment loads from the sub-watershed. These same locations in this study generated a low I<sub>APCW</sub>.展开更多
Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, A...Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec<sup>®</sup></sup> Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.展开更多
Significant land-use changes in North Dakota have been reported and are widespread over the entire state. Such changing patterns may portend localized impairment to agricultural watersheds. In this study, Land-use Lan...Significant land-use changes in North Dakota have been reported and are widespread over the entire state. Such changing patterns may portend localized impairment to agricultural watersheds. In this study, Land-use Land-cover (LULC) change was modeled using geostatistics. The study area was within the Pipestem Creek watershed, a part of the Missouri Watershed James Subregion of North Dakota, USA. Landsat Thematic mapper images from the years 2007, 2011 and 2015 were used as preliminary data. LULC information for these datasets was acquired from the Global Land-cover facility and Landsat Program. Data analysis, spectral classification and post classification techniques were applied on the datasets. A transition matrix was derived using a Markov chain Monte Carlo (MCMC) model. This study demonstrates that the integration of satellite remote sensing, GIS and statistics may be an effective approach for analyzing the direction, rate, and spatial pattern of land-use change.展开更多
文摘Spatial causal effects on water quality are essential in identification of vulnerable watersheds. Modelling landuse variables is an effective method of projecting localized impairment. This study presents an integrated index, designed to gauge the ability of an 8-digit Hydrologic Unit Code watershed in its ability to produce clean water. This index, I<sub>APCW</sub>, can be successfully applied on a geospatial platform. In this study we utilized I<sub>APCW</sub> to address forest cover dynamics of an impaired watershed, that is, Missouri Watershed James Sub-region in North Dakota. Specific parametric functions were analysed and combined within a customized GIS interface to provide a multi-faceted structured technique to derive I<sub>APCW</sub>. These included ambient forest cover, housing density, agricultural land, soil erodibility and road density;it can be lucidly ascertained that where a prevailing forest cover undergoes conversion processes, the secondary effect may spur an exponential increase in water treatment costs. These parameters when projected statistically validated temporal and spatial relations of landuse/land cover dynamics to nutrient concentrations especially those that would be noted at the mouth of the watershed. In this study, we found that the levels of Total Dissolved Solids (TDS) were much higher for the years 2014 to 2016 with a discernible increased alkalizing effect within the watershed. When I<sub>APCW</sub> was compared to Annualized Agricultural Nonpoint Source (AnnAGNPS), the spatial distribution generated by the AnnAGNPS study showed that fallow areas produced significant amounts of sediment loads from the sub-watershed. These same locations in this study generated a low I<sub>APCW</sub>.
文摘Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec<sup>®</sup></sup> Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.
文摘Significant land-use changes in North Dakota have been reported and are widespread over the entire state. Such changing patterns may portend localized impairment to agricultural watersheds. In this study, Land-use Land-cover (LULC) change was modeled using geostatistics. The study area was within the Pipestem Creek watershed, a part of the Missouri Watershed James Subregion of North Dakota, USA. Landsat Thematic mapper images from the years 2007, 2011 and 2015 were used as preliminary data. LULC information for these datasets was acquired from the Global Land-cover facility and Landsat Program. Data analysis, spectral classification and post classification techniques were applied on the datasets. A transition matrix was derived using a Markov chain Monte Carlo (MCMC) model. This study demonstrates that the integration of satellite remote sensing, GIS and statistics may be an effective approach for analyzing the direction, rate, and spatial pattern of land-use change.
