Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja,Nigeria

This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis.     

Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing

Vast tracts of forests are lost globally every year especially in the developing countries of the tropics due to various human activities such as lumbering, farming, bush fires, surface mining and urbanization. The rainforest in Ghana has experienced rapid depletion since the 1980s. The impact of deforestation is widespread, affecting the livelihoods of local people and disrupting the tropical ecosystem. There is a serious concern in the study area about climatic change, soil erosion, siltation of rivers and loss in biodiversity which have an adverse impact on traditional medicinal plants of the local people. The study examined the extent of land cover change through image differencing of Landsat TM 1986 and 2002. The image classification indicated that, vegetative cover from 1986 to 2002 has been reducing whiles land use activities have been increasing. Closed canopy, open canopy and plantation have significantly diminished and land use activities especially built ups, farms, mining and openfields are more than doubled. The driving forces for the change in land cover are population growth, lumbering, socio-economic and cultural practices of the people. Lumbering and mining have been some of the major causes of the changing landscape in primary forest. Also the reliance on wood for domestic energy and the need to increase food productivity to feed growing population have also contributed greatly to the rapid depletion of the vegetative cover.     

近10年洞庭湖区土地利用变化时空特征

The research on the land use/cover change is one of the frontiers and the hot spots in the global change research. Based on the Chinese resource and environment spatial-temporal database,and using the Landsat TM and ETM data of 1990 and 2000 respectively, we analyzed the spatial-temporal characteristics of land use/cover changes in the Dongting Lake area during the last decade. The result shows that during the last ten years there were three land-use types that had changed remarkably. The cultivated land decreased by 0.57% of the total cultivated land. The built-up land and water area expanded, with an increase of 8.97% and 0.43% respectively. The conversion between land use types mostly happened among these three land-use types, especially frequently between cultivated land and water area. The land-use change speed of land-use type is different. Three cities experienced the greatest degree of land-use change among all the administrative districts, which means that the land use in these cities changed much quickly. The following changed area was the west and south of the Dongting Lake area. The slowest changed area is the north and east area.     

Sensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model

We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one‐at‐a‐time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non‐linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs.     

光谱与纹理信息复合的土地利用/覆盖变化动态监测研究

及时、准确地动态监测地球表面特性对于掌握人类和自然现象之间的关系和相互作用是非常重要的,并为决策的制定奠定基础。传统卫星遥感的土地利用/覆盖变化动态监测方法基本上可分为光谱直接比较法和分类比较法两大类,但两类方法多以光谱信息为基础来提取土地利用变化信息,而忽略纹理等空间信息。本研究中,基于变化向量分析方法,将光谱与纹理两种信息复合计算变化强度,并采用支持向量机法提取变化/非变化信息,通过监督分类确定变化区域内的土地利用/覆盖类型的转移方向,完成土地利用/覆盖动态监测。最后,利用两期TM数据,对海淀区1997—2004年进行土地利用/覆盖变化动态监测,以验证该方法。该方法较分类后比较法在一定程度上减少误差积累,降低了错误类型转化,提取的变化信息总精度达到93.1%,Kappa为0.862,比利用光谱信息双窗口变步长的变化向量分析方法提取出土地利用/覆盖变化信息的精度有一定的提高(总体精度为90.2%,Kappa为0.804)。纹理信息与光谱信息复合,能够更大拉开变化/非变化信息之间的距离,有利于动态变化信息的提取,是该方法能够有效提取变化信息的关键所在。     

地理加权回归在区域土地利用格局模拟中的应用——以常州市孟河镇为例

以江苏省常州市新北区孟河镇为研究区,在土地利用格局模拟的回归建模中考虑驱动因子对土地利用格局影响的空间不稳定性,实现基于地理加权的回归分析模型,并与基于全局最小二乘法(OLS)的Logistic回归模型进行比较。研究结果表明,运用地理加权回归(GWR)的建模方法,不但可以获得更好的拟合优度和更高的拟合准确率,而且可以获得各驱动因子对土地利用格局影响的空间分异特征。同时,研究结果也可以为孟河镇及其类似地区的土地利用规划决策提供科学依据。     

Sensitivity of surface air temperature change to land use/cover types in China

Using CRU high resolution grid observational temperature and ERA40 reanalysis surface air temperature data during 1960–1999, we investigated the sensitivity of surface air temperature change to land use/cover types in China by subtracting the reanalysis from the observed surface air temperature (observation minus reanalysis, OMR). The results show that there is a stable and systemic impact of land use/cover types on surface air temperature. The surface warming of each land use/cover type reacted differently to global warming. The OMR trends of unused land (⩾0.17 °C/decade), mainly comprised by sandy land, Gobi and bare rock gravel land, are obviously larger than those of the other land use/cover types. The OMR over grassland, farmland and construction land shows a moderate decadal warmingabout 0.12°C/decade, 0.10°C/decade, 0.12°C/decade, respectively. Woodland areas do not show a significant warming trend (0.06°C/decade). The overall assessment indicates that the surface warming is larger for areas that are barren and anthropogenically developed. The better the vegetation cover, the smaller the OMR warming trend. Responses of surface air temperature to land use/cover types with similar physical and chemical properties and biological processes have no significant difference. The surface air temperature would not react significantly until the intensity of land cover changes reach a certain degree. Within the same land use/cover type, areas in eastern China with intensive human activities exhibit larger warming trend. The results provide observational evidence for modeling research on the impact of land use/cover change on regional climate. Thus, projecting further surface climate of China in regional scale should not only take greenhouse gas increase into account, but also consider the impact of land use/cover types and land cover change. Supported by National Basic Research Program of China (Grant No. 2005CB422006), National Natural Science Foundation of China (Grant Nos. 90202012, 40771206)     

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

ABSTRACT

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.     

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.     

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment,South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F_4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F_2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.