Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images

Ning, Shu‐Kuang; Chang, Ni-Bin; Jeng, Kai-Yu; Tseng, Yi Hsing

doi:10.1016/j.jenvman.2005.05.019

Cited by 62 publications

(28 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The supervised land use classification method was performed, with training data that was collected in a companion study (Ning et al, 2006). The 12 LULC classes mapped included 1) conifers, 2) broad-leaf trees, 3) mixture of conifer and broad-leaf trees, 4) bamboo, 5) grassland, 6) dry farmland, 7) paddy field, 8) orchard, 9) urban, 10) barren, 11) water body, and 12) others.…”

Section: Preparation Of Biogenic Emission Datamentioning

confidence: 99%

Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model

Cheng¹,

Chang

2009

Front. Earth Sci. China

Self Cite

View full text Add to dashboard Cite

In many metropolitan regions, natural sources contribute a substantial fraction of volatile organic compound (VOC) emissions. These biogenic VOC emissions are precursors to tropospheric Ozone (O 3 ) formation. Because forests make up 59% of the land area in Taiwan Province, China, the biogenic VOC emissions from forests and farmland could play an important role in photochemical reactions. On the other hand, anthropogenic emissions might also be one of the major inputs for ground level O 3 concentrations. Hence, emission inventory data, grouped as point, area, mobile and biogenic VOC sources, are a composite of reported and estimated pollutant emission information and are used by many air quality models to simulate ground level O 3 concentrations. Before using relevant air quality models, the emission inventory data generally require huge amounts of processing for spatial, temporal, and species congruence with respect to the associated air quality modeling work. The fist part of this research applied satellite remote sensing and geographic information system (GIS) analyses to characterize land use/land cover (LULC) patterns, integrating various sources of anthropogenic emissions and biogenic emissions associated with a variety of plant species. To investigate the significance of biogenic VOC emissions on ozone formation, meteorological and air quality modeling were then employed to generate hourly ozone estimates for a case study of a high ozone episode in southern Taiwan, which is the leading industrial hub on the island. To enhance the modeling accuracy, a unique software module, SMOKE, was set up for emission processing to prepare emission inputs for the U.S. EPA's Models-3/CMAQ. An emission inventory of Taiwan, TEDS 4.2, was used as the anthropogenic emission inventory. Biogenic emission modeling was accomplished by BEIS-2 in SMOKE, with improvement of local LULC data and revised emission factors. Research findings show that the majority of biogenic VOC emissions occur in the mountainous areas and farmlands. However, the modeling outputs show that downwind of the most heavily populated and industrialized areas, these biogenic VOC emissions have less impact on air quality than do anthropogenic emissions.

show abstract

Section: Preparation Of Biogenic Emission Datamentioning

confidence: 99%

Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model

Cheng¹,

Chang

2009

Front. Earth Sci. China

Self Cite

View full text Add to dashboard Cite

show abstract

“…GIS tools were used to prepare and process input-output vectors data for the ANN, which efficiently simulated groundwater NO 3 -concentrations and captured the general trend of groundwater NO 3 -pollution patterns (Wang et al 2006). The use of multi-temporal RS images in support of environmental modelling analysis in a GIS environment has contributed to identify a variety of long-term interactions between resources and land use (Ning et al 2006). The spatial and temporal variation in NO 3 -in the basin of a small river was simulated by combining NO 3 -dynamic modelling and GIS with the use of RS NDVI (Matejicek et al 2003).…”

Section: Integration Of Rs and Gis With Simulation Models For Watershmentioning

confidence: 99%

Incorporating water quantity and quality modelling into forest management

Li¹,

Nour²,

Smith³

et al. 2008

The Forestry Chronicle

View full text Add to dashboard Cite

Under the authority of the Millar Western Forest Products Ltd. (MWFP) Forest Management Agreement, the company has the right to harvest trees, grow trees, manage the forest and plan activities that assure forest productivity and industry profitability without jeopardizing the quality of the environment. Thus, as part of obtaining provincial government approval, the company has to submit a Detailed Forest Management Plan that includes a comprehensive assessment of the environmental implications of forestry operations and the mitigation of impacts. Forest management planning for environmental sustainability will become more and more difficult with increased land use pressures from other industries, agriculture and recreation. Therefore, the planning process will require increasingly more sophisticated modelling tools to identify and avoid significant impacts. The Forest Watershed and Riparian Disturbance (FORWARD) project proposes a hybrid modelling tool that relies on inexpensive remote sensing data, with few ground truthing requirements, to model streamflow, suspended solids and nutrients in streams on the Boreal Plain. Incorporating modelling tools into the MWFP planning process provides MWFP additional strategies to operate in an environmentally sensitive manner. Thus, the company can maintain an allowable cut, while ensuring that ecological and physical values are considered.

show abstract

“…It has negative effects on the environment and leads to reduced crop productivity, worsened water quality, lower effective reservoir water levels, flooding, and habitat destruction (Lee 2004;Oh and Jung 2005;Park et al 2011). Furthermore, soil erosion is considered an essential source of non-point source pollution for the water bodies in many terrestrial environments (Ning et al 2006;Wu et al 2012). Additionally, because erosion leads to the removal of the soil's organic carbon and clay content, eroded sediments transporting as much as 20 % carbon can be released into the atmosphere as CO 2 (Lal 1995;Lal and Bruce 1999;Yang et al 2003), thereby reducing soil's ability to mitigate the green house effect (Yang et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization

Zhang

Huang

2014

Environ Sci Pollut Res

View full text Add to dashboard Cite

Soil erosion has become a pressing environmental concern worldwide. In addition to such natural factors as slope, rainfall, vegetation cover, and soil characteristics, land-use changes-a direct reflection of human activities-also exert a huge influence on soil erosion. In recent years, such dramatic changes, in conjunction with the increasing trend toward urbanization worldwide, have led to severe soil erosion. Against this backdrop, geographic information system-assisted research on the effects of land-use changes on soil erosion has become increasingly common, producing a number of meaningful results. In most of these studies, however, even when the spatial and temporal effects of land-use changes are evaluated, knowledge of how the resulting data can be used to formulate sound land-use plans is generally lacking. At the same time, land-use decisions are driven by social, environmental, and economic factors and thus cannot be made solely with the goal of controlling soil erosion. To address these issues, a genetic algorithm (GA)-based multi-objective optimization (MOO) approach has been proposed to find a balance among various land-use objectives, including soil erosion control, to achieve sound land-use plans. GA-based MOO offers decision-makers and land-use planners a set of Pareto-optimal solutions from which to choose. Shenzhen, a fast-developing Chinese city that has long suffered from severe soil erosion, is selected as a case study area to validate the efficacy of the GA-based MOO approach for controlling soil erosion. Based on the MOO results, three multiple land-use objectives are proposed for Shenzhen: (1) to minimize soil erosion, (2) to minimize the incompatibility of neighboring land-use types, and (3) to minimize the cost of changes to the status quo. In addition to these land-use objectives, several constraints are also defined: (1) the provision of sufficient built-up land to accommodate a growing population, (2) restrictions on the development of land with a steep slope, and (3) the protection of agricultural land. Three Pareto-optimal solutions are presented and analyzed for comparison. GA-based MOO is found able to solve the multi-objective land-use problem in Shenzhen by making a tradeoff among competing objectives. The outcome is alternative choices for decision-makers and planners.

show abstract

Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images

Cited by 62 publications

References 29 publications

Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model

Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model

Incorporating water quantity and quality modelling into forest management

Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization

Contact Info

Product

Resources

About