Distribution Of Land Cover Types Research Articles

LAND COVER DYNAMICS OF DIFFERENT TOPOGRAPHIC CONDITIONS IN BEIJING

Topographic conditions play an important role in controlling land cover dynamic processes. In this study, remotely sensed data and geographic information system were applied to analyze the changes in land cover along topographic gradients from 1978 to 2001 in Beijing, a rapidly urbanized mega city in China. The study was based on five periods of land cover maps derived from remotely sensed data, Landsat MSS for 1978, Landsat TM for 1984, 1992, 1996 and 2001, and digital elevation model (DEM) derived from a 1: 250 000 topographic map. The entire area was divided into 10 land cover types: conifer forest, broadleaf forest, mixed forest, shrub, brushwood, meadow, farmland, built_up, water body and bare land. The results are summarized below. 1) Shrub, forest, farmland and built_up consist of the main land cover types of the Beijing area. The most significant land cover change from 1978 to 2001 was a decrease in farmland and an expansion of built_up areas. Farmland decreased from 6 354 to 3 813 km~2 over the 23 years, while built_up areas increased from 421 to 2 642 km~2. Forest cover increased from 17.2% to 24.7% of the total area. The conversion matrix analysis indicated that transformation of farmland to built_up area was the most significant process and afforestation was the primary cause of replacement of shrub to forest. 2) Topographic conditions are of great importance to the distribution of land cover types and the process of land cover changes. Elevation has a strong impact on the distribution of land cover types. The area below 100 m mostly consisted of farmland and built_up areas, and the area above 100 m is mainly covered by shrub and forest. Shrub has a maximum frequency in elevations between 100 and 1 000 m, while forests were dominant in areas above 800 m. According to the analysis of land cover changes in different elevation ranges, the greatest change was below 100 m due to urbanization. The main land cover change process that occurred above 100 m was transformation from shrub to forest. This result was constant with the vertical change of natural vegetation distribution in Beijing. 3) Slope has a large influence on the distribution of land cover types. Farmland and built_up areas are distributed primarily in flat areas, while shrub and forest occupy steeper lands as compared with other land cover types. Forest frequency increased with increasing slope. Land cover change processes differed with slope gradients. In the plain area, land cover change occurred as the result of urbanization. With increasing slope gradients, afforestation, which converted shrub to forest, was the primary land cover change process.

Climate and satellite‐derived land cover for predicting breeding bird distribution in the Great Lakes Basin

AbstractAim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km2). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models.Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA.Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate).Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species.Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.

Spatial Optimization of Wildlife Habitat — Model Formulation

The spatial distribution of land cover types affects the suitability of an area to wildlife species. Modification of cover types can be used to achieve a prescribed wildlife response. A nonlinear programming model was developed for the design of a cost-effective plan of cover type modification.