Wavelet-based detection of bush encroachment in a savanna using multi-temporal aerial photographs and satellite imagery

Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes.     

Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

安徽省含山县土地整理耕地质量评价及其变化研究

土地整理是改善土地利用条件,提高土地质量的有效措施,对土地整理后的耕地质量进行评价是土地整理重要工作之一。从耕地质量的内涵和土地整理项目的基本要求出发,选取耕地自然条件和耕地利用条件2个基本因素和12个影响因子,将特尔斐法与层次分析法相结合,确定各因子权重,构建基于土地整理的耕地质量评价指标体系。以安徽省含山县铜闸镇土地整理项目为例,采用传统的土壤调查与3S技术相结合的方法,分析土地整理复垦过程中耕地质量时空格局变化,得出整理前、后耕地质量的级别变化。研究结果表明,通过土地整理,研究区的耕地质量空间分布和级别发生了很大变化,整理后一级、二级、三级质量的土地比整理前分别增加了7.20%,11.95%和0.65%,四级、五级则分别减少了15.65%和4.16%,整理后的耕地质量总体水平是整理前的1.291倍。     

基于遥感技术的近30年中亚地区主要湖泊变化

在全球气候变暖的背景下,研究中亚干旱半干旱地区主要湖泊的变化不仅对内陆水资源管理和可持续发展有着重要意义,也为进一步研究湖泊变化对气候变化和人类活动的响应提供基础。获取1978年MSS、1989年TM、1998年TM及2010年ETM的同季相4期遥感图像数据,通过人工解译提取中亚地区7个湖泊信息,获得近30 a的湖泊面积变化;利用T/P和Envisat雷达高度计提取1992—2012年的湖泊水位信息;基于湖泊面积和水位的时空变化特征分析了湖泊变化的影响因素。结果表明:近30 a来,由于湖泊类型的不同,除萨雷卡梅什湖外的其他6个湖泊均出现不同程度的面积缩减;其中,平原尾闾湖面积变化最显著,高山封闭湖相对平稳,吞吐湖泊的面积变化相对复杂;湖泊的水位变化模式因流域和湖泊类型而异;高山封闭湖泊受气候的影响较大,而吞吐湖泊受人为因素影响显著。     

视觉感受与Markov随机场相结合的高分辨率遥感影像分割法

鉴于视觉感受对外界强大的感知与识别能力,模拟视觉神经感知的工作机制,并结合Markov随机场模型,提出一种影像分割方法。首先,分析视觉感知系统的工作机制,将其特性归纳为等级层次性、学习能力、特征检测能力和稀疏编码特性,继而利用小波变换、非监督聚类、特征分析和Laplace分布模拟视觉工作机制,然后结合Markov随机场模型实现高分辨率遥感影像的分割。通过不同卫星的真实遥感影像进行了相关试验。试验结果表明本文提出的方法在高分辨率遥感影像分割任务中有非常良好的表现。     

ZY-102C星数据在矿山遥感监测中的应用研究与分析

利用资源一号(ZY-1)02C卫星(以下简称02C星)全色高分辨率相机(HR)和多光谱相机(MUX)数据,以河北承德多金属矿区和江西寻乌稀土矿区为实验区,开展了1∶5万矿山遥感监测应用研究;分析了应用过程中存在的问题,提出了解决方案和建议,对02C星数据在矿山遥感监测中的规模化应用起到了示范效果。研究结果表明:在数据处理流程上,对空间分辨率为2.36 m的02C星全色HR1与HR2数据需先进行正射纠正再拼接,对空间分辨率为10 m的多光谱数据MUX可采用假彩色合成方法进行模拟真彩色合成;在矿山地物识别上,与SPOT5图像比较,02C星图像可更好地反映出开采面、开采点、中转场和矿山建筑等矿山地物类型,不足之处是纹理信息较平滑、矿山地物的边界相对模糊,因而增加了室内解译的难度;在经济效益上,02C星数据的性价比优势明显。因此,尽管02C星数据处理和解译工作量较大,但其能满足1∶5万矿山遥感监测要求,总体经济效益显著。     

高分辨率遥感图像道路分割算法

为了提高从高分辨率遥感图像(high-resolution remote sensing image,HRI)中提取道路信息的自动化程度和准确性,发展了一种HRI道路分割算法,主要包括光谱合并、边界合并和基于形状特征的道路区域提取等3个步骤。其中,前2个步骤是基于区域生长的图像分割算法。光谱合并综合考虑了区域的均值、方差等统计特征量,以提高分割精度;边界合并采用了基于矢量梯度的边界计算方法,以准确提取多光谱HRI中的边界强度;结合全局最优合并算法实现光谱和边界合并,以得到最优化的分割结果。在道路区域被完整分割出来的基础上,利用形状特征提取道路,采用圆形度特征区分道路和非道路。利用2景Orb View3多光谱图像进行道路提取实验的结果表明,该方法的道路提取结果总精度和Kappa系数分别在97%和0.8以上,明显优于SVM监督分类方法。     

Tracking pollutants from space: Eight years of IASI satellite observation

The IASI mission flying onboard the MetOp satellites has been providing global observations of the air composition twice a day since 2007. From the atmospheric spectra recorded by the instruments in the thermal infrared spectral range, concentrations of a series of trace gases can be monitored, enhanced levels of pollution can be detected, and particle types can be determined to some extent. This paper recalls the historical context for the IASI remote sensor, reviews its capability to observe some key species for global and regional pollution monitoring, and reports on information services that benefit from the mission.     

基于卫星遥感和地面观测资料的汾渭平原空气污染分析

选取中国汾渭平原地区作为研究对象,利用MODIS、OMI和CALIPSO多源卫星遥感资料,同时结合环境监测国控站点污染6要素等逐小时地面环境监测数据以及能见度、霾天气现象记录等地面气象要素资料,综合分析了2013—2018年秋冬季汾渭平原空气质量状况、气溶胶的组分,探讨了卫星遥感气溶胶光学厚度（Aerosol Optical Depth,AOD）与地面污染物浓度的关系,并结合中国气象局化学天气预报系统-EMI评估模式（CUACE-EMI）资料对气象条件和污染减排影响进行评估。结果表明：11个代表城市中有6个城市秋冬季有接近或超过一半的时刻处于污染状态,且污染发生时,各代表城市大多数时刻处于中度及其以上污染级别;三门峡、临汾、运城和西安是霾和重度霾高发的城市,其重度霾爆发频率高达11.63%—14.78%;汾渭平原秋冬季首要污染物为PM_2.5和PM₁₀,以污染沙尘、沙漠沙尘和烟尘为主,出现频率分别为36.24%、25.14%和22.96%;MODIS AOD与空气质量指数（Air Quality Index,AQI）、PM_2.5、PM₁₀质量浓度之间的相关系数分别为0.72、0.70和0.64;汾渭平原2018年气象条件的变化使PM_2.5浓度较2013年、2014年、2015年、2016年和2017年同期上升了17.06%、1.58%、4.34%、11.25%和5.75%,减排措施使PM_2.5浓度较2013年、2014年、2015年、2016年和2017年同期分别下降了8.74%、28.01%、4.93%、3.16%和42.62%。