RIDGE REGRESSION TECHNIQUES APPLIED TO LANDSAT INVESTIGATION OF WATER QUALITY IN LAKE OKEECHOBEE1

ABSTRACT: Landsat radiance values were processed at two different (single and double) levels of accuracy to estimate chlorophyll a, turbidity, and suspended sediment in Lake Okeechobee, Florida. Both ordinary least square and ridge regression analyses were used to establish a relationship between water quality parameters and Landsat radiance. Radiance measurements made at greater precision (double level) gave a better solution in this application. The ridge regression analysis for double level not only can reduce the total mean square error about 13–20 percent and confidence interval about 6–28 percent as compared to ordinary least square analysis, but it can also change the interpretation of analysis results.     

Sampling and Mapping a Soil Erosion Cover Factor by Integrating Stratification,Model Updating and Cokriging with Images

Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation cover factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the cover factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the cover factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.     

塔里木河下游区域植被时空变化

为了对比生态输水工程实施前后塔里木河下游植被变化,分析植被对生态输水的时空响应,基于1986-2017年研究区上方无云的22期Landsat NDVI数据,辅以波动分析、趋势分析,探索研究区植被的时空变化特征,并对其驱动因素进行分析。结果表明：（1）空间上,植被的分布与水资源的分布密切相关,主要沿河道和积水区域分布。（2）1986-2000年,NDVI呈现缓慢下降趋势,2000-2017年NDVI总体呈现波动增加趋势;植被对生态输水有一定的滞后性,且与输水量密切相关。（3）研究区NDVI时间序列的方差介于0.0077~0.1957间,存在明显的空间差异,其波动的大小与植被种类密切相关。（4）1986-2000年,植被的NDVI空间趋势以减小为主,并主要发生在英苏和老英苏附近;2000-2017年,植被的NDVI空间趋势以增加为主,且主要发生在沿河道区域和喀尔达依东侧。     

Assessing wetland changes in the venice lagoon by means of satellite remote sensing data

Not only does lagoon ecology represent a transitional zone between the sea and the continent but it also expresses the equilibrium belt between erosion and sedimentation processes. Within the framework of a coastal management scheme, a precise and timely mapping of morphological changes in this environment is important. This paper illustrates the possible contribution of multi-temporal satellite observations in the monitoring of the erosion/sedimentation processes of coastal zones, where landscape features are subjected to highly morphodynamical modifications. In particular, an improved mapping accuracy was obtained by the successive application of the Maximum Likelihood (MLH) classifier and the Linear Mixture Model (LMM) techniques to the satellite image classification procedure. In fact, by estimating the amount of shallow water and wetland within each satellite pixel, the LMM technique allows for an accurate mapping of the transitional zones in the lagoon environment, thus permitting an optimal separation between land and water. The study concerns the Venice lagoon (Italy) which has been sinking slowly since the beginning of this century. This has led to widespread loss of wetlands. In order to monitor the development of the land cover, four Landsat Thematic Mapper scenes were examined, during the period 1984 to 1993. The results obtained proved that the digital analysis method of multitemporal satellite imagery, applied over a selected test area, enables the evolution of an estuarine environment landscape, with its different sequences of erosion and periods of accretion, to be monitored. The significant influence of tidal stages is discussed in the data analysis.     

Use of Airborne Thermal Imagery to Detect and Monitor Inshore Oil Spill Residues During Darkness Hours

/ Trials were conducted using an airborne video system operating in the visible, near-infrared, and thermal wavelengths to detect two known oil spill releases during darkness at a distance of 10 nautical miles from the shore in St. Vincent's Gulf, South Australia. The oil spills consisted of two 20-liter samples released at 2-h intervals, one sample consisted of paraffinic neutral material and the other of automotive diesel oil. A tracking buoy was sent overboard in conjunction with the release of sample 1, and its movement monitored by satellite relay. Both oil residues were overflown by a light aircraft equipped with thermal, visible, and infrared imagers at a period of approximately 1 h after the release of the second oil residue. Trajectories of the oil residue releases were also modeled and the results compared to those obtained by the airborne video and the tracking buoy. Airborne imagery in the thermal wavelengths successfully located and mapped both oil residue samples during nighttime conditions. Results from the trial suggest that the most advantageous technique would be the combined use of the tracking beacon to obtain an approximate location of the oil spill and the airborne imagery to ascertain its extent and characteristics.KEY WORDS: Airborne video; Thermal imagery; Global positioning; Oil-spill monitoring; Tracking beacon     

基于机器学习和卫星遥感的PM2.5/10 空间连续分布反演方法研究

在过去的几十年里,快速的经济发展以及工业化、城镇化进程加速使得中国的资源环境承担的压力不断加大。作为影响空气质量的首要污染物,PM_2.5和PM₁₀（记为PM_2.5/10）直接影响着广大人民群众的身体健康。因此,针对PM_2.5/10浓度进行遥感反演研究,对环境监测和控制改善全国空气环境质量具有重要的意义。近些年来,随着对近地面PM_2.5/10浓度研究的不断深入,基于遥感影像数据进行PM_2.5/10浓度的反演方法也日益增多。本文利用Google Earth engine（GEE）平台获取了海量的Landsat 8 OLI遥感影像数据,并结合气象信息、空间特征等参数,采用机器学习中常用的多层映射反向传播神经网络构建了波段反射率与PM_2.5/10浓度之间的反演模型,以获得PM_2.5/10在研究区域的连续分布。为了提高基础PM_2.5/10反演模型的反演精度,还从影响因素和前溯时间两个维度出发,探寻了模型的最优化输入参数组合,并最终实现了对PM_2.5/10浓度的精准反演。以北京市地区为例,模型的PM_2.5和PM₁₀的反演精度R²分别达到0.814和0.796,均方根误差RMSE分别为19.21 μg?m^?3和28.31 μg?m^?3。鉴于该反演结果具有较高的准确性和可靠性,本文所建立的方法模型为研究PM_2.5/10在空间上的连续分布特征提供了新的思路和方法,具有较为重要的科研意义与广泛的应用价值。     

基于面向对象的海岸带土地利用信息提取研究

针对海岸带复杂多样的土地利用信息,选取山东省乳山市海岸带为研究区域,运用 Landsat TM遥感影像数据,基于面向对象分类方法,利用不同地物的光谱、形状、纹理和空间关系等特征,通过多尺度分割、隶属度函数法和标准最邻近分类法提取研究区土地利用信息,并对分类结果进行精度评估。结果表明：①隶属度函数法和标准最邻近分类法结合,提取出乳山市海岸带12 种土地利用类型信息,很好地区分了盐田和养殖水面、林地和园地,可提取出主要的道路和河流等细长线状地物;②将提取结果与最大似然法对比,面向对象分类方法提取精度达到82.50%,Kappa系数为0.809 1,分别比最大似然法提高了11.44%和0.105 5,很好地避免“同物异谱”和“异物同谱”对分类精度造成的影响,有效地避免了“椒盐”现象。面向对象分类方法提取中分辨率遥感影像精度较高,为海岸带土地利用信息的快速、准确提取提供了有效的技术手段。     

城市公园的冷岛效应研究——以常州市为例

在长江三角洲地区快速城市化的常州市内研究公园组成与冷岛效应的量化关系.基于Landsat 8TIRS和天地图高分辨率遥感影像提取公园指标以及冷岛效应指标.使用辐射传输方程法反演研究区的地表温度,使用ArcGIS的缓冲区分析和分段回归的方法研究每个公园的地表温度、降温范围和降温幅度作为冷岛效应指标;提取公园的周长、面积,并计算融合指数作为公园组成指标,在天地图高清遥感影像中提取公园的绿地覆盖率、水体覆盖率、不透水面覆盖率以及乔木覆盖率作为公园地表覆盖指标.通过相关性分析和回归模型研究,发现公园的面积为26hm²、周长阈值为3600m,公园的面积和周长在阈值范围内能够发挥较强的降温效率,超过阈值则降温效率下降.公园的融合指数与冷岛效应呈线性相关关系,融合指数较小的公园能获得较大的降温幅度.公园的乔木覆盖率、水体覆盖率与公园内部的地表温度呈显著相关关系,不透水面覆盖率、绿地覆盖率与公园内部的地表温度相关性不显著.公园规划设计需要结合公园的面积、周长阈值,融合指数的数值以及乔木、水体的覆盖率,充分发挥公园的冷岛效应.     

基于知识的AVHRR影像的水体自动识别方法与模型研究

根据水体光谱特性，通过分析水体在ＮＯＡＡ气象卫星的ＡＶＨＲＲ影像上的表象特征及与其它相关环境因子在光谱信息上的差异，认为水体在ＡＶＨＲＲ影像上具有光谱可分辨性，从而提出了基于水体光谱知识的水体自动提取识别的水体描述模型，并将该模型应用于太湖、淮河、渤海等地区，取得较好的效果。该模型方法同样可扩展应用于其它传感器获得的影像数据，从而实现遥感信息提取的自动化与智能化     

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.