Land cover changes and landscape pattern dynamics in Senegal and Guinea Bissau borderland

Different levels of climatic, agricultural, demographic, political and socioeconomic change have been detected in West African countries. For many years the human impact on the region was negligible, due to the low population density. However, in the last decades, demographic increase has accelerated exploitation of the natural resources and consequently the degradation of the ecosystems. Land cover degradation and landscape changes are more or less pronounced according to politic and socio-economic conditions of each country. In this study, a region located on the borderland of north Guinea-Bissau and south Senegal, is analyzed with regard to land cover and landscape trends, considering the socio-economic factors that drive them and the borderline effect. In this analysis, remote sensing data (Landsat TM, ETM+ and OLI data) were used to obtain four land cover maps for the years 1990, 2002, 2010 and 2015. The results show a similar landscape behavior in both countries, directly related to similar socio-economic practices. Forest area increase in both countries and there is a reduction in Agriculture/Bare soil areas until 2010. Main causes may be associated to the rural exodus, resulting from the conflict for independence of Casamance, which affects the borderland region of Senegal and Guinea Bissau, or to the conversion of agricultural areas into cashew orchards. In opposition, between 2010 and 2015, Forest area decrease and Agriculture/Bare soil increase, due to the recent trend of conversion of old cashew orchards into traditional fields of cereals and peanut. An increase in landscape fragmentation is observed in the period under analysis, revealing a heterogenization trend, which can be related to the adoption of similar human practices, in the last decades.     

大范围倾斜多视影像连接点自动提取的区域网平差法

提出了一种大范围倾斜多视影像自动连接点提取和光束法区域网平差算法。首先利用POS数据对倾斜影像进行矫正并预测候选像对,使用SIFT特征进行影像匹配;然后利用并查集数据结构实现多视影像间连接点对应关系的快速确定;最后将POS数据和控制点数据作为带权观测值进行平差解算。选取3种典型的倾斜相机系统(组装轻量级倾斜相机、国产SWDC-5相机、微软UltraCam相机)进行了试验,结果表明,本文算法能够适应国内外流行的倾斜摄影系统,且一次性可处理的影像数目超过2000张;在相同条件下倾斜影像光束法区域网平差的整体精度优于传统垂直摄影情形,实际精度可以达到水平方向0.75 GSD,高程方向2.0GSD。     

Quantification of the Total Suspended Matter Concentration around the Sea Breaking Zone from In Situ Measurements and Terra/Aster Data

Remote sensing techniques can be used to quantify the total suspended matter concentration (TSM). The main objective of this study is the quantification of the TSM concentration around the sea breaking zone for a particular area of the Portuguese coast, near Aveiro city. The methodology used was based on in situ measurements and on multispectral satellite image from TERRA/ASTER. In situ experimental techniques were used to determine a relationship between the TSM concentration and the seawater reflectance. Empirical relationships were established between TSM concentration and the equivalent reflectance values for TERRA/ASTER at visible and near infrared bands computed from the experimental data. The reflectance values had a very high correlation with the TSM concentration in the wavelength between 500 and 900 nm. The ASTER image was calibrated, atmospherically and geometric corrected. Equations of linear, polynomial, logarithmic, power and exponential models were applied. The best results were found with the linear and polynomial models, with a determination factor higher than 0.95. However, the differences in the TSM concentration calculated for the three bands indicate that we should not use the information of a single band to obtain the TSM concentration.     

Processing Multibeam Backscatter Data

A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research.     

SPOT 5卫星遥感影像在土地利用数据库系统更新中的应用

随着国民经济的快速发展,土地现状变更十分频繁,如何保持数据库系统的现势性成为国土资源管理部门的一项重要工作。该文介绍了利用卫星遥感数据对土地利用数据库变更的系统设计、实现方法及结果,说明了利用遥感卫星数据对数据库进行更新的优势。     

基于气象卫星的青藏高原低涡识别

利用长时间序列气象卫星及多源数据,研究青藏高原低涡综合识别方法,完成低涡数据集并与青藏高原低涡年鉴中低涡位置、路径和分布进行对比分析。研究表明:卫星识别多年平均低涡分布存在两个高值区,分别位于西藏的中北部和青海西南部及青藏高原西部,在有探空站的青藏高原东部（90°E以东）,卫星识别低涡高值区和年鉴数据吻合,冬半年,卫星识别低涡活动明显高于年鉴,主要为青藏高原西部低涡活动引起,逐年及2008年低涡路径对比也显示,有探空站区域卫星识别低涡和年鉴具有较好的一致性,表明卫星识别低涡在青藏高原东部地区的可信性;2015年青藏高原中西部新增3个探空站,年鉴中90°E以西低涡约占全年低涡总数量的22%,该区域卫星识别低涡和年鉴一致性较高,表明卫星识别低涡在高原中西部的可信性。因此,卫星识别低涡与年鉴低涡在有探空站区域有较好的一致性,可对年鉴中青藏高原东部低涡源地进行追踪,又可识别青藏高原中西部尤其是活跃于冬半年的低涡,是青藏高原年鉴低涡数据的有效补充。     

基于20年卫星遥感资料的黄海、渤海海雾分布季节特征分析

目前对海上雾分布的认识多基于沿岸测站和海上船舶、浮标观测,但这些数据非常稀少,且存在代表性和数据质量方面的问题,因此一直缺乏对海雾分布更全面、清晰的了解。卫星遥感数据空间均一、覆盖范围广、质量一致,具有对无云条件下大范围、离岸海雾监测的优势。本文通过分析算法检测出的1989-2008年黄渤海海雾及云的频数、分布百分率信息,得到了黄渤海海雾季节变化的较全面特征。除印证其他资料或研究的结论外,还发现:(1)黄海海雾频数随季节变化的幅度较渤海明显;(2)黄海、渤海海域存在冬季海雾多发时段;(3)海雾生消过程中有覆盖区变化的东传特征;(4)春夏雾季中存在黄海中部和西朝鲜湾两处海雾多发区,其中西朝鲜湾也是全年海雾最多的海域。另外,在样本充足的情况下,通过对检测出的低云、中高云覆盖百分率和海雾频数的分析统计,还能估算出黄海、渤海部分季节20年海雾发生的平均概率。     

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.     

Evaluation of SPOT imagery for the estimation of grassland biomass

In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R² values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.     

结合分形理论和自适应图像块划分的遥感图像噪声估计

针对场景复杂的光学遥感图像中加性噪声估计问题,提出了一种结合分形理论和自适应图像块划分的噪声估计方法。区别于传统的基于规则图像块划分的噪声估计方法,本文提出了一种自适应于图像局部信息的图像块划分算法,更大程度地保证了自适应图像块内部的平滑性。结合基于分形理论的图像低粗糙度纹理区域选取和基于统计分析的图像噪声标准差计算,实现了光学遥感图像加性噪声强度的自动估计。利用资源三号卫星图像进行定量试验分析,试验结果表明本文方法可以有效地适用于不同复杂程度、不同噪声强度的光学遥感图像。同时,本文中低粗糙度纹理区域选取和自适应图像块划分的方法经过改进后,还可以应用于雷达图像中乘性噪声的估计。