Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.     

Crossing-over between land cover and land use: Exploring spatially varying relationships in two large US metropolitan areas

Difficulties in identifying actual uses of land space from remote sensing-based land cover products often result in lost opportunities to enhance the capacity of applied research on human settlements. In an attempt to address these difficulties, this study investigates how land cover and land use are interrelated with each other and what determines the relationship patterns by analyzing detailed land use and land cover data for two large US metropolitan areas – the five-county Los Angeles and six-county Chicago regions – where a broad spectrum of human settlements, ranging from urban cores to less-urbanized edges, coexist. The analysis shows that the land cover-land use relationship substantially varies not only across regions but across neighborhoods within each region. Through multivariate regression, it is also found that the intraregional variation is highly associated with the neighborhood's stage of urbanization, median housing age, and other development characteristics, suggesting that the relationship pattern can largely be shaped by the history and evolution of urban design/development.     

Spatiotemporal patterns of tropical deforestation and forest degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km² neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988–1995 (620 km² per year), 1995–2001 (591 km² per year), and 2001–2008 (660 km² per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km–80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.     

多目标珊瑚岛礁地貌遥感信息提取方法——以西沙永乐环礁为例

南海珊瑚岛礁资源极为丰富,实时、快速、高效、准确地获取大范围珊瑚岛礁地貌遥感信息具有现实意义.研究提出了一种双尺度转化下的模型与数据混合驱动的岛礁地貌信息提取框架,并设计了珊瑚岛礁地貌分类体系及相应技术流程:首先采用自上而下模型驱动的GVF Snake模型进行宏观地理分带的粗分割,然后采用自下而上数据驱动的云影极值抑制下多阈值OTSU分类算法进行微观地貌类型的精细分类,最终利用区域生长算法提取离散分布的暗沙、暗滩等浅水地貌单元.针对西沙永乐环礁利用CBERS-02B数据进行实验,精度验证表明:珊瑚岛礁地貌遥感信息提取方法总体精度优于经典数据驱动的监督分类算法,且具有抗噪能力强、顾及空间拓扑关系、自动灵活等特点.     

基于Landsat 8劈窗算法与混合光谱分解的城市热岛空间格局分析——以兰州市中心城区为例

在ENVI和GIS支持下,提出了基于Landsat 8遥感影像的地温反演劈窗算法,提取兰州市中心城区地表温度。利用FNEA和混合光谱分解法确定了兰州市中心城区的城市热岛中心、不透水面和植被盖度,分析了城市热岛空间分布格局以及地表温度与下垫面之间的关系。结果显示:基于Landsat 8数据地温反演的劈窗算法是可行的。兰州中心城区的高温区分布较集中,地表温度与植被呈较强的负相关,与不透水面呈不显著的正相关,与其他非光合物质呈正相关。     

中国南方湿润区“荒漠化”问题讨论

荒漠化是当今世界所面临的严重的环境与社会经济问题,推进荒漠化研究工作对于保护生态环境,促进区域可持续发展具有重要意义。本文在对荒漠化内涵进行系统回顾的基础上,认为除了人为干扰叠加气候性干旱作用导致的荒漠化之外,南方湿润区因水土流失叠加物理性干旱作用导致的极端土地退化也是一种荒漠化,并从物质基础、气候条件、影响因子、形成营力、核心作用和地表过程方面进行比较,最后以南方红层区特殊的"红层荒漠化"为案例,利用实地调查和TM影像的植被覆盖度指标VFC对1992-2013年红层土地退化进行分析。结果显示,在人为反复干扰和其他自然因素作用下地表植被持续退化、群落干旱趋势明显,表土层流失,岩石风化加剧,以致形成类似"荒漠"景观的现象。红层区的退化过程可类比于喀斯特石漠化、红壤山地丘陵退化,但这类极端土地退化现象可否视为"荒漠化",有待于进一步的深入研究。     

利用卫星遥感技术估算区域性海-气CO_2通量研究综述

海-气CO2通量的估算是碳循环研究的重要内容。随着卫星遥感技术的发展和外推算法的不断改进,基于海表层水CO2分压的调控影响因子,在不同的海域发展了不同参数的海表p CO2遥感外推算法,获得了区域性多时空尺度长时间序列的海域碳"源-汇"时空变化特征。本文针对单参数算法、双参数算法和多参数算法的发展过程和存在问题,综述了主要海区海-气CO2通量遥感外推算法的研究现状,并评述了在南大洋和北极海域利用遥感算法进行海-气CO2通量研究的应用。     

利用MODIS可见光波段反演陆地气溶胶光学厚度

利用Herold等建立的地表反射率库及MODIS遥感影像研究城市区和非城市区典型地物在可见光红蓝波段地表反射率的比值特性。在此基础上,利用MODIS 1km分辨率遥感影像红蓝可见光波段实现了气溶胶光学厚度的反演,采用卫星过境时间前后半小时北京和香河AERONET站的气溶胶光学厚度观测平均值作为验证参考。结果显示,66.67%的反演结果处于±0.05±0.15τ的误差界限内,反演算法不受地表反射率的限制,而且只利用了可见光红蓝波段,避免缺少近红外波段数据的限制。     

Evaluating the relationship between biomass,percent groundcover and remote sensing indices across six winter cover crop fields in Maryland,United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r² = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r² = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.     

Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.