SSM/I微波辐射计数据中国陆地覆盖特征季节变化分析

利用星载微波辐射计SSM/I多通道、多时相亮温数据开展了中国陆地覆盖特征的季节变化研究。通过对国内外星载微波辐射应用研究分析 ,提出了归一化极化指数 (NDPI)的概念。处理了 1 997年 4月、7月、1 0月和 1 998年 1月的 (每月的 2 0、2 4日各两天 )多通道SSM/I数据 ,在此基础上计算形成了第一幅中国陆地区域的归一化微波极化指数图 ,开展了中国陆地区域覆盖特征随季节变化的研究。研究结果表明 ,不同的陆地覆盖特征有特征的NDPI值 ,NDPI随季节而变化 ,植被。

Brovey图像融合在引大灌区土地覆盖调查中的应用

图像融合是目前广泛应用的一种增强图像信息的技术手段 ,其目标是将不同几何和不同质的图像通过某种数学和物理变换生成相同几何或另一种均质的新图像。以引大灌区土地利用调查为例 ,针对灌区的土地类型特点以及对空间分辨率的要求 ,选择 SPOT全色波段和 TM多光谱波段 ,利用 Brovey图像融合方法进行图像融合 ,以获得 1 0 m空间分辨率的假彩色合成图像。同时 ,对融合的图像进行精度验证 。

Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area

The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.

Influence of urbanization on the thermal environment of meteorological station:Satellite-observed evidence

In this paper, five national meteorological stations in Anhui province are taken as typical examples to explore the effects of local urbanization on their thermal environment by using Landsat data from 1990 to 2010. Satellite-based land use/land cover(LULC), land surface temperature(LST), normalized difference vegetation index(NDVI) are used to investigate the effects. The study shows that LULC around meteorological stations changed significantly due to urban expansion. Fast urbanization is the main factor that affects the spatial-temporal distribution of thermal environment around meteorological stations. Moreover, the normalized LST and NDVI exhibit strong inverse correlations around meteorological stations, so the variability of LST can be monitored through evaluating the variability of NDVI. In addition, station-relocation plays an important role in improving representativeness of thermal environment. Notably, the environment representativeness was improved, but when using the data from the station to study climate change, the relocation-induced inhomogeneous data should be considered and adjusted. Consequently,controlling the scale and layout of the urban buildings and constructions around meteorological stations is an effective method to ameliorate observational thermal environment and to improve regional representativeness of station observation. The present work provides observational evidences that high resolution Landsat images can be used to evaluate the thermal environment of meteorological stations.

Using Landsat images to monitor changes in the snow-covered area of selected glaciers in northern Pakistan

Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.

Spatiotemporal Dynamics of Forest and Mountain Formations in Auras Area, Algeria

Contrary to the countries of northern coast, the forest formations on Southern and East coast of the Mediterranean are in regression. With the combined action of the ax, fire and pasture, these ecosystems are subjected to a pressure of permanent degradation. Indeed, the degradation of the forest ecosystems represents one of the most important causes of reduction and erosion of the biodiversity in the world. The massif of Aur6s is located in the North-East of Algeria. The analysis of the spatiotemporal dynamics of this vulnerable vegetation is not approached yet. This study proposes a focus on the analysis of the dynamics of this vegetation and to study the factors of its degradation. For that, a methodological approach of diachronic follow-up between two dates was adopted by using any kind of old information sources （archives, aerial photographs, topographic maps） and recent （Images satellite of American Landsat and data of land）. The results reveal a degradation of vegetable cover thus expressing a very thorough reduction of the formations forest replaced by herbaceous formations very sensitive and threatened by the overgrazing.

Effects of historical land cover changes on climate

In order to explore the influence of anthropogenic land use on the climate system during the last mil- lennium, a set of experiments is performed with an Earth system model of intermediate complexity—— the McGill Paleoclimate Model (MPM-2). The present paper mainly focuses on biogeophysical effects of historical land cover changes. A dynamic scenario of deforestation is described based on changes in cropland fraction (RF99). The model simulates a decrease in global mean annual temperature in the range of 0.09-0.16℃, especially 0.14-0.22℃ in Northern Hemisphere during the last 300 years. The responses of climate system to GHGs concentration changes are also calculated for comparisons. Now, afforestation is becoming an important choice for the enhancement of terrestrial carbon sequestration and adjustment of regional climate. The results indicate that biogeophysical effects of land cover changes cannot be neglected in the assessments of climate change.

Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia

Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature(LST) and Normalized Difference Vegetation Index(NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081?C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.