首页 | 官方网站   微博 | 高级检索  
相似文献
 共查询到10条相似文献,搜索用时 156 毫秒
1.
This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands.  相似文献   

2.
Landsat urban mapping based on a combined spectral-spatial methodology   总被引:1,自引:0,他引:1  
Urban mapping using Landsat Thematic Mapper (TM) imagery presents numerous challenges. These include spectral mixing of diverse land cover components within pixels, spectral confusion with other land cover features such as fallow agricultural fields and the fact that urban classes of interest are of the land use and not the land cover category. A new methodology to address these issues is proposed. This approach involves, as a first step, the generation of two independent but rudimentary land cover products, one spectral-based at the pixel level and the other segment-based. These classifications are then merged through a rule-based approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of Ottawa, Calgary and cities in southwestern Ontario is presented based on conventional ground reference data as well as inter-classification consistency analyses. Producer accuracies of 78% and 73% have been achieved for urban ‘residential’ and ‘commercial/industrial’ classes, respectively. The capability of Landsat TM to detect low density residential areas is assessed based on dwelling and population data derived from aerial photography and the 2001 Canadian census. For low population densities (i.e. below 3000 persons/km2), density is observed to be monotonically related to the fraction of pixels labeled ‘residential’. At higher densities, the fraction of pixels labeled ‘residential’ remains constant due to Landsat's inability to distinguish between high-rise apartment dwellings and commercial/industrial structures.  相似文献   

3.
Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km2), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m− 2 year− 1 (15 m data) to 431 gC m− 2 year− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.  相似文献   

4.
Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO2 emission from daily urban travel at the disaggregate level in urban Beijing.  相似文献   

5.
Although cities, towns and settlements cover only a tiny fraction (< 1%) of the world's surface, urban areas are the nexus of human activity with more than 50% of the population and 70-90% of economic activity. As such, material and energy consumption, air pollution, and expanding impervious surface are all concentrated in urban areas, with important environmental implications at local, regional and potentially global scales. New ways to measure and monitor the built environment over large areas are thus critical to answering a wide range of environmental research questions related to the role of urbanization in climate, biogeochemistry and hydrological cycles. This paper presents a new dataset depicting global urban land at 500-m spatial resolution based on MODIS data (available at http://sage.wisc.edu/urbanenvironment.html). The methodological approach exploits temporal and spectral information in one year of MODIS observations, classified using a global training database and an ensemble decision-tree classification algorithm. To overcome confusion between urban and built-up lands and other land cover types, a stratification based on climate, vegetation, and urban topology was developed that allowed region-specific processing. Using reference data from a sample of 140 cities stratified by region, population size, and level of economic development, results show a mean overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R2 = 0.90).  相似文献   

6.
基于土地利用数据的人口统计数据空间化方法,在处理过程中会出现同一土地利用类型下人口难以细分的情况,从而影响人口空间数据精度。引入夜间灯光信息并提出了一种基于夜间灯光强度对城镇居民地再分类的人口空间化方法,以改善人口空间数据精度。基于DMSP/OLS夜间灯光及土地利用数据,以长江中游4省为研究区进行方法试验。研究结果显示:利用夜间灯光数据对城镇居民地再分类后,各分区模型的调整R2都提高到了0.8以上,人口空间数据总体平均相对误差较重分类前降低了12.32%。说明该方法在提高传统人口数据空间化模型精度的基础上能够细化城镇居民地人口空间分布。  相似文献   

7.
Land cover maps provide essential input data for various hydromorphological and ecological models, but the effect of land cover classification errors on these models has not been quantified systematically. This paper presents the uncertainty in hydromorphological and ecological model output for a large lowland river depending on the classification accuracy (CA) of a land cover map. Using four different models, we quantified the uncertainty for the three distributaries of the Rhine River in The Netherlands with respect to: (1) hydrodynamics (WAQUA model), (2) annual average suspended sediment deposition (SEDIFLUX model), (3) ecotoxicological hazards of contaminated sediment for a bird of prey, and (4) floodplain importance for desired habitat types and species (BIO-SAFE model). We carried out two Monte Carlo (n = 15) analyses: one at a 69% land cover CA, the other at 95% CA. Subsequently we ran all four models with the 30 realizations as input.The error in the current land cover map gave an uncertainty in design water levels of up to 19 cm. Overbank sediment deposition varied up to 100% in the area bordering the main channel, but when aggregated to the whole study area, the variation in sediment trapping efficiency was negligible. The ecotoxicological hazards, represented by the fraction of Little Owl habitat with potential cadmium exposure levels exceeding a corresponding toxicity threshold of 148 μg d−1, varied between 54 and 60%, aggregated over the distributaries. The 68% confidence interval of floodplain importance for protected and endangered species varied between 10 and 15%. Increasing the classification accuracy to 95% significantly lowered the uncertainty of all models applied. Compared to landscaping measures, the effects due to the uncertainty in the land cover map are of the same order of magnitude. Given high financial costs of these landscaping measures, increasing the classification accuracy of land cover maps is a prerequisite for improving the assessment of the efficiency of landscaping measures.  相似文献   

8.
Despite great advancements in recent years, the availability of detailed and regionalised farm practice data at national scale remains an obstacle for spatially-detailed research on sustainable intensification. Parsing and information retrieval techniques were applied to 385 farm management handbooks to estimate farm practices (use of fertilisers, pesticides, water, fuel) of 72 commodities grown in 42 regions. Life-cycle inventories were used to derive GHG emissions and energy use from farm practice data. Practices and impacts were mapped at 1.1 km2 resolution using agricultural census data and a remote-sensing-based land use map. Existing data was linearly extrapolated using a rule-based approach to fill spatial gaps. Estimates were, in aggregate, comparable to the best available data at national and local scales. Our method contributes to the push to create more spatially-detailed assessments of agricultural impacts at a national scale by focusing on the production of basic data at the farm level.  相似文献   

9.
The MODIS (Moderate Resolution Imaging Spectroradiometer) primary productivity products are evaluated against observed Above-ground Net Primary Production (AGNPP) in the semi-arid Senegal 2001. MODIS net primary productivity (NPP) modelling is a light use efficiency (LUE) based approach incorporating constraints on vegetation productivity arising from simulated radiation, water demand and temperature data from NASA's Data Assimilation Office (DAO). Annually integrated MODIS PSN (MOD17A2 net photosynthesis, Collection 4) explains more of the observed biomass variation (r2 = 0.77) than MODIS fAPAR (fraction Absorbed Photosynthetically Active Radiation, Collection 4) (r2 = 0.72), indicating the effect of including the canopy stress scalar (εs) based on DAO data combined with modelled maintenance respiration costs (of leaf and fine roots). Annual MODIS NPP (MOD17A3, Collection 4 (C4) and Collection 4.5 (C4.5)) including growth respiration and live wood maintenance respiration costs and modified DAO input (C4.5) however increases the residual unexplained observed AGNPP variance (C4 NPP; r2 = 0.49) (C4.5 NPP; r2 = 0.37). The overall quality of the annual NPP MODIS C4 and C4.5 products are moderate for the semi-arid Senegal because of the annual respiration cost modelling and a change in C4.5 biome-specific parameters stored in a Biome Properties Look-Up Table (BPLUT) is the main contributor to the observed discrepancy between C4 and C4.5 NPP. The dynamic range of the values of all MOD17 products was too low when compared to observed AGNPP. An estimate of canopy water stress (SIWSI; Shortwave Infrared Water Stress Index) derived from MODIS channels 2 and 6 and photosynthetically active radiation (PAR) irradiance derived from geostationary METEOSAT data were tested for primary production modelling using a stepwise linear regression analysis. PAR irradiance was combined with MODIS fAPAR into APAR (Absorbed Photosynthetically Active Radiation) explaining 79% of the observed AGNPP variation. Introducing SIWSI significantly increased the explained variance of observed AGNPP (r2 = 0.89). MODIS-derived percentage tree cover was tested as a predictor based on the hypothesis that tree cover provides information on differences in respiratory costs between trees and grasses thereby accounting for variations in the LUE conversion efficiency ε. No significant reduction in residual unexplained AGNPP variance was found. Earth observation based derivation of PAR and canopy water stress from SIWSI suggest potential improvements to primary production models in semi-arid biomes that can be implemented in general NPP modelling LUE methodology.  相似文献   

10.
There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km2 up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 103 and 140 × 103 km2, respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号