Land Use And Land Cover Data Research Articles

Quantifying the effect of land use and land cover changes on green water and blue water in northern part of China

Abstract. Changes in land use and land cover (LULC) have been occurring at an accelerated pace in northern parts of China. These changes are significantly impacting the hydrology of these parts, such as Laohahe Catchment. The hydrological effects of these changes occurring in this catchment were investigated using a semi-distributed hydrological model. The semi-distributed hydrological model was coupled with a two-source potential evaportranspiration (PET) model for simulating daily runoff. Model parameters were calibrated using hydrometeorological and LULC data for the same period. The LULC data were available for 1980, 1989, 1996 and 1999. Daily streamflow measurements were available from 1964 to 2005 and were divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005. These periods represented four different LULC scenarios. Streamflow simulation was conducted for each period under these four LULC scenarios. The results showed that the change in LULC influenced evapotranspiration (ET) and runoff. The LULC data showed that from 1980 to 1996 grass land and water body had decreased and forest land and crop land had increased. This change caused the evaporation from vegetation interception and vegetation transpiration to increase, whereas the soil evaporation tended to decrease. Thus during the period of 1964–1979 the green water or ET increased by 0.95%, but the blue water or runoff decreased by 8.71% in the Laohahe Catchment.

Land Surface Temperature Variation and Major Factors in Beijing, China

Land surface temperature (LST) is a significant parameter in urban environmental analysis. Current research mainly focuses on the impact of land-use and land-cover (LULC) on LST. Seldom has research examined LST variations based on the integration of biophysical and demographic variables, especially for a rapidly developing city such as Beijing, China. This study combines the techniques of remote sensing and geographic information system (GIS) to detect the spatial variation of LST and determine its quantitative relationship with several biophysical and demographic variables based on statistical modeling for the central area of Beijing. LST and LULC data were retrieved from a Landsat Thematic Mapper (TM) image. Building heights were delimited from the shadows identified on a panchromatic SPOT image. The integration of LULC and census data was further applied to retrieve grid-based population density. Results indicate that the LST pattern was non-symmetrical and non-concentric with high temperature zones clustered towards the south of the central axis and within the fourth ring road. The percentage of forest, farmland, and water per grid cell were found to be most significant factors, which can explain 71.3 percent Of LST variance, Principal component regression analysis shows that LST Was positively correlated with the percentage of low density built-up, high density built-up, extremely-high buildings, low buildings per grid cell, and population density, but was negatively correlated with the percentage of forest, farmland, and water bodies per grid cell. The findings of this study can be applied as the theoretical basis for improving urban planning for mitigating the effects of urban heat islands.

Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston Metropolitan area: Part II: Air quality simulation results

In the companion paper, we showed that MM5 simulation using a satellite-derived high resolution Texas Forest Service (TFS) land use and land cover (LULC) data set (M2), compared to the MM5 results with the default USGS-LULC (M1), improved representation of the complicated features of the atmospheric planetary boundary layer (PBL) in the Houston ship channel (HSC) area, where large industrial emission sources are concentrated. In the present paper, the study is extended to investigate these effects on air quality simulations. Two emission inputs, namely E1 and E2, are prepared with the M1 and M2 meteorology data, respectively, to reflect the differences in the point source plume rise estimates while keeping the biogenic and mobile emissions the same. Air quality simulations were performed with CMAQ using the M1E1 and M2E2 inputs. The simulation results demonstrate the importance of utilizing high resolution LULC data. In the default LULC data, the HSC area was classified as grass land cover, and MM5 predicted confined mixing, resulting in over-prediction of ozone (O 3) precursors, such as NO x (NO plus NO 2), and highly reactive volatile organic compounds (HRVOC) species, including ethylene and propylene, over the HSC area. In the TFS data, the area was classified as the impervious “urban” land use and MM5 predicted enhanced mixing of the precursor species, leading to better agreements with measurements. The high resolution LULC also resolves the location of water body near the HSC more accurately, predicting shallower PBL heights than the default LULC during daytime. With favorable wind conditions, the O 3 precursors were transported from the HSC emission source towards the area, trapping the pollutants in a confined shallow mixing layer that occasionally led to a rapid photochemical production of O 3. The above comparison includes the changes in both meteorological and plume-rise emissions inputs. We performed two additional CMAQ simulations using the same meteorological result (M2) but with different emission point sources E1 and E2 to determine the importance of emission changes on the air quality simulations. The sensitivity tests with the different plume-rise emission inputs due to two different meteorological inputs show little impact on the air quality simulations in this case.

Land‐cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling

Land use and land‐cover (LULC) data provide essential information for environmental management and planning. This research evaluates the land‐cover change dynamics and their effects for the Greater Mankato Area of Minnesota using image classification and Geographic Information Systems (GIS) modelling in high‐resolution aerial photography and QuickBird imagery. Results show that from 1971 to 2003, urban impervious surfaces increased from 18.3% to 32.6%, while cropland and grassland decreased from 54.2% to 39.1%. The dramatic urbanization caused evident environmental impacts in terms of runoff and water quality, whereas the annual air pollution removal rate and carbon storage/sequestration remained consistent since urban forests were steady over the 32‐year span. The results also indicate that highly accurate land‐cover features can be extracted effectively from high‐resolution imagery by incorporating both spectral and spatial information, applying an image‐fusion technique, and utilizing the hierarchical machine‐learning Feature Analyst classifier. This research fills the high‐resolution LULC data gap for the Greater Mankato Area. The findings of the study also provide valuable inputs for local decision‐makers and urban planners.