基于土壤数据和遥感影像的鄂尔多斯高原北部地貌分类(英文)

Landform classification is commonly done using topographic altitude only.However,practice indicates that locations at a same altitude may have distinctly different landforms,depending on characteristics of soils underneath those locations.The objectives of this study were to:1) develop a landform classification approach that is based on both altitude and soil characteristic;and 2) use this approach to determine landforms within a watershed located in northern Ordos Plateau of China.Using data collected at 134 out of 200 sampling sites,this study determined that D10(the diameter of soil particles 10% finer by weight) and long-term average soil moisture acquired in 2010,which can be estimated at reasonable accuracy from remote sensing imagery,can be used to represent soil characteristics of the study watershed.Also,the sampling data revealed that this watershed consists of nine classes of landforms,namely mobile dune(MD),mobile semi-mobile dune(SMD),rolling fixed semi-fixed dune(RFD),flat sandy land(FD),grassy sandy land(GS),bedrock(BR),flat sandy bedrock(FSB),valley agricultural land(VA),and swamp and salt lake(SW).A set of logistic regression equations were derived using data collected at the 134 sampling sites and verified using data at the remaining 66 sites.The verification indicated that these equations have moderate classification accuracy(Kappa coefficients > 43%).The results revealed that the dominant classes in the study watershed are FD(36.3%),BR(27.0%),and MD(23.5%),while the other six types of landforms(i.e.,SMD,RFD,GS,FSB,VA,and SW) in combination account for 13.2%.Further,the landforms determined in this study were compared with the classes pre-sented by a geologically-based classification map.The comparison indicated that the geo-logically-based classification could not identify multiple landforms within a class that are de-pendent upon soil characteristics.     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.     

Using remote sensing and GIS to investigate the impacts of tourism on forest cover in the Annapurna Conservation Area,Nepal

Tourism is Nepal's largest industry, giving people in rural areas an alternative to subsistence farming. Tourism can have an impact on the forest cover of a region as trees provide firewood for cooking and heating and timber for building accommodation. In 1986 the Annapurna Conservation Area Project was established to ensure that tourism was managed more sustainably, which includes minimising its impact on the forest cover. This study assesses the impacts of tourism on the forest cover in the Annapurna region by comparing Landsat images from 1999 to 2011. This was achieved through spectral classification of different land cover and by assessing the change in forest cover in relation to increasing distances from tourism villages. A major problem with remote sensing in mountainous regions such as Nepal is shadow caused by the relief. This issue was addressed by only assessing areas which were free from shadow, which in effect meant a sample was used rather than the whole study region. The results indicate that there has been an 8 percent reduction in overall forest extent, but this change varies by region. In the northern, drier regions there has been a net increase in forest cover, while in the southern regions there has been a net reduction in forests. The influence of tourism facilities on forest is also variable. Around each of the tourism villages sampled there was a general trend of forest removal decreasing as the distance from each village increased, which indicates tourism does have a negative impact on forests. However, there was an opposite trend in the northern villages that were well inside the conservation area.