Mapping chlorophyll-a through in-situ measurements and Terra ASTER satellite data

This paper presents an application of water quality mapping through real-time satellite and ground data. The Lake Beysehir which is the largest freshwater lake and drinking water reservoir in Turkey was selected as the study area. Terra ASTER satellite image is used as remote sensing data source for water quality mapping in addition to simultaneously performed in-situ measurements. Ground data is collected simultaneously with the ASTER overpass on June 09, 2005 over the Lake Beysehir. The spatial distribution map is developed by using multiple regression (MR) technique for water quality parameter, which is chlorophyll-a (chl-a). The results indicate that simultaneous ground and satellite remote sensing data are highly correlated (R (2) > 0.86). In the image processing step, geometric correction, image filtering and development of water quality map procedures are performed with the ERDAS Imagine and ArcGIS 9.0 software. The trophic status of Lake Beysehir is considered to be oligotrophic with an average 1.55 microg/l chl-a concentration.     

Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).     

基于MODIS的烟台近海水体叶绿素浓度分布

利用实测光谱数据及水体叶绿素浓度数据建立了基于MODIS数据的叶绿素反演模型,并利用MODIS L1B数据对研究区的叶绿素浓度进行了反演。通过分析烟台近海水体叶绿素浓度分布得出,烟台近海水体叶绿素浓度由沿岸向海延伸,叶绿素的浓度逐渐增加;通过不同月份的叶绿素浓度分布状况发现,夏季水体叶绿素浓度含量最高,冬季最低。     

Water quality assessment at Ömerli Dam using remote sensing techniques

Water quality at Omerli Dam, which is a vital potable water resource of Istanbul City, Turkey was assessed using the first four bands of Landsat 7-ETM satellite data, acquired in May 2001 and water quality parameters, such as chlorophyll-a, suspended solid matter, secchi disk and total phosphate measured at several measurement stations at Omerli Dam during satellite image acquisition time and archived at the Marine Pollution and Ecotoxicology laboratory of the Marmara Research Center, where this study was carried out. Establishing a relationship between this data, and the pixel reflectance values in the satellite image, chlorophyll-a, suspended solid matter, secchi disk and total phosphate maps were produced for the Omerli Dam.     

Mapping urban forest tree species using IKONOS imagery: preliminary results

A stepwise masking system with high-resolution IKONOS imagery was developed to identify and map urban forest tree species/groups in the City of Tampa, Florida, USA. The eight species/groups consist of sand live oak (Quercus geminata), laurel oak (Quercus laurifolia), live oak (Quercus virginiana), magnolia (Magnolia grandiflora), pine (species group), palm (species group), camphor (Cinnamomum camphora), and red maple (Acer rubrum). The system was implemented with soil-adjusted vegetation index (SAVI) threshold, textural information after running a low-pass filter, and brightness threshold of NIR band to separate tree canopies from non-vegetated areas from other vegetation types (e.g., grass/lawn) and to separate the tree canopies into sunlit and shadow areas. A maximum likelihood classifier was used to identify and map forest type and species. After IKONOS imagery was preprocessed, a total of nine spectral features were generated, including four spectral bands, three hue?Cintensity?Csaturation indices, one SAVI, and one texture image. The identified and mapped results were examined with independent ground survey data. The experimental results indicate that when classifying all the eight tree species/ groups with the high-resolution IKONOS image data, the identifying accuracy was very low and could not satisfy a practical application level, and when merging the eight species/groups into four major species/groups, the average accuracy is still low (average accuracy = 73%, overall accuracy = 86%, and ???=?0.76 with sunlit test samples). Such a low accuracy of identifying and mapping the urban tree species/groups is attributable to low spatial resolution IKONOS image data relative to tree crown size, to complex and variable background spectrum impact on crown spectra, and to shadow/shaded impact. The preliminary results imply that to improve the tree species identification accuracy and achieve a practical application level in urban area, multi-temporal (multi-seasonal) or hyperspectral data image data should be considered for use in the future.     

An examination of the effects of land use changes on nature conservation rulings in Çeşme peninsula, Turkey

Because of their intense vegetation and the fact that they include areas of coastline, deltas situated in the vicinity of big cities are areas of great attraction for people who wish to get away from in a crowded city. However, coasts, with their fertile soil and unique flora and fauna, need to be protected. In order for the use of such areas to be planned in a sustainable way by local authorities, there is a need for detailed data about these regions. In this study, the changes in land use of the area between Topburnu and Uçburun Musa Bey Harbour on the Çe?me peninsula, which is to the immediate west of Turkey’s third largest city ?zmir, from 1976 up to the present day, were investigated. In the study, using aerial photographs taken in 1976, 1995 and 2000 and an IKONOS satellite image from the year 2007, the natural and cultural characteristics of the region and changes in the coastline were determined spatially. Using aerial photographs from 1976, 1995 and 2000 and an IKONOS satellite image from the year 2007, together with “1/25,000 scale Conservation-Oriented Development Plans” prepared in 1979, 1990 and 2000 by the committee for the Preservation of Natural and Cultural Entities attached to the Ministry of Culture and Tourism of the Turkish Republic, the natural and cultural characteristics of the region and the land use changes and their connection with conservation rulings were determined spatially. In this study, spatial changes in land use over the years were compared with changing conservation rulings over the years and the emerging results have brought a new perspective to the subject in contrast to other similar studies.     

基于MODIS的西藏典型湖泊叶绿素a浓度反演

叶绿素a浓度是反映湖泊富营养化状态的一个重要参数。以MODIS L1B数据为基础，结合叶绿素a浓度实测数据，基于经验分析法实现了西藏典型湖泊叶绿素a浓度反演研究，并探索了西藏典型湖泊2019年春、夏、秋季叶绿素a浓度的时空变化特征。首先，利用叶绿素a浓度实测数据和MODIS L1B影像不同波段的反射率值进行组合试验，选择最佳波段组合建立模型；其次，分别选用2015年、2017年叶绿素a浓度实测值和反演值对模型进行对比验证；最后，利用叶绿素a浓度反演模型对西藏典型湖泊2019年春、夏、秋季叶绿素a浓度的时空变化特征进行分析。结果表明：在空间尺度上，西藏典型湖泊叶绿素a浓度整体上呈现出周围高、中部低的分布特征，且湖岸水体叶绿素a浓度变化较大；在季节尺度上，不同湖泊叶绿素a浓度的季节变化存在较大差异，格仁错和色林错的季节变化幅度较大，纳木错、塔若错和羊卓雍错的季节变化幅度较小。     

基于“哨兵3号”卫星OLCI影像和C2RCC算法的南黄海叶绿素a及总悬浮物反演效果分析

利用遥感数据处理软件SNAP中基于神经网络技术的C2RCC算法,对2019年5月9日南黄海“哨兵3号”卫星OLCI影像数据进行了叶绿素a及总悬浮物浓度反演,将其与5月间江苏省海洋环境监测预报中心的海水表层叶绿素a和悬浮物实测数据进行了比较分析。结果表明,叶绿素a的遥感反演尚未能达到业务化应用,总悬浮物遥感反演结果的空间分布与实测值的一致性相对较好。但在星地同日或相差一天监测的南通海域,遥感反演叶绿素a浓度的空间分布趋势以及总悬浮物遥感反演结果与海面实测结果的一致性较好,可达到一定的业务化应用效果。     

Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island,Malaysia

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.     

Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling

Groundwater development across much of sub-Saharan Africa is constrained by a lack of knowledge on the suitability of aquifers for borehole construction. The main objective of this study was to map groundwater potential at the country-scale for Ghana to identify locations for developing new supplies that could be used for a range of purposes. Groundwater potential zones were delineated using remote sensing and geographical information system (GIS) techniques drawing from a database that includes climate, geology, and satellite data. Subjective scores and weights were assigned to each of seven key spatial data layers and integrated to identify groundwater potential according to five categories ranging from very good to very poor derived from the total percentage score. From this analysis, areas of very good groundwater potential are estimated to cover 689,680 ha (2.9 % of the country), good potential 5,158,955 ha (21.6 %), moderate potential 10,898,140 ha (45.6 %), and poor/very poor potential 7,167,713 ha (30 %). The results were independently tested against borehole yield data (2,650 measurements) which conformed to the anticipated trend between groundwater potential and borehole yield. The satisfactory delineation of groundwater potential zones through spatial modeling suggests that groundwater development should first focus on areas of the highest potential. This study demonstrates the importance of remote sensing and GIS techniques in mapping groundwater potential at the country-scale and suggests that similar methods could be applied across other African countries and regions.     

Three dimensional monitoring of urban development by means of ortho-rectified aerial photographs and high-resolution satellite images

Nowadays, cities are developing and changing rapidly due to the increases in the population and immigration. Rapid changing brings obligation to control the cities by planning. The satellite images and the aerial photographs enable us to track the urban development and provide the opportunity to get the current data about urban. With the help of these images, cities may have interrogated dynamic structures. This study is composed of three steps. In the first step, orthophoto images have been generated in order to track urban developments by using the aerial photographs and the satellite images. In this step, the panchromatic (PAN), the multi spectral (MS) and the pan-sharpened image of IKONOS satellite have been used as input satellite data and the accuracy of orthophoto images has been investigated in detail, in terms of digital elevation model (DEM), control points, input images and their properties. In the second step, a 3D city model with database has been generated with the help of orthophoto images and the vector layouts. And in the last step, up to date urban information obtained from 3D city model. This study shows that it is possible to detect the unlicensed buildings and the areas which are going to be nationalized and it also shows that it is easy to document the existing alterations in the cities with the help of current development plans and orthophoto images. And since accessing updated data is very essential to control development and monitor the temporal alterations in urban areas, in this study it is proven that the orthophoto images generated by using aerial photos and satellite images are very reliable to use in obtaining topographical information, in change detection and in city planning. When digital orthophoto images used with GIS, they provide quick decision control mechanisms and quick data collection. Besides, they help to find efficient solutions in a short time in the planning applications.     

Evaluating Change in Rangeland Condition using Multitemporal AVHRR Data and Geographic Information System Analysis

Coarse-scale, multitemporal satellite image data were evaluated as a tool for detecting variation in vegetation productivity, as a potential indicator of change in rangeland condition in the western U.S. The conterminous U.S. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data set was employed using the six-year time series 1989–1994. Normalized Difference Vegetation Index (NDVI) image bands for the state of New Mexico were imported into a Geographic Information System (GIS) for analysis with other spatial data sets. Averaged NDVI was calculated for each year, and a series of regression analyses were performed using one year as the baseline. Residuals from the regression line indicated 14 significant areas of NDVI change: two with lower NDVI, and 11 with higher NDVI. Rangeland management changes, cross-country military training activities, and increases in irrigated cropland were among the identified causes of change.     

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO₄ phosphorus, NO₃ nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.     

Surface geophysical exploration: developing noninvasive tools to monitor past leaks around Hanford’s tank farms

A characterization program has been developed at Hanford to image past leaks in and around the underground storage tank facilities. The program is based on electrical resistivity, a geophysical technique that maps the distribution of electrical properties of the subsurface. The method was shown to be immediately successful in open areas devoid of underground metallic infrastructure, due to the large contrast in material properties between the highly saline waste and the dry sandy host environment. The results in these areas, confirmed by a limited number of boreholes, demonstrate a tendency for the lateral extent of the underground waste plume to remain within the approximate footprint of the disposal facility. In infrastructure-rich areas, such as tank farms, the conventional application of electrical resistivity using small point-source surface electrodes initially presented a challenge for the resistivity method. The method was then adapted to directly use the buried infrastructure, specifically the steel-cased wells that surround the tanks, as “long” electrodes for both transmission of electrical current and measurements of voltage. Overcoming the drawbacks of the long electrode method has been the focus of our work over the past 7 years. The drawbacks include low vertical resolution and limited lateral coverage. The lateral coverage issue has been improved by supplementing the long electrodes with surface electrodes in areas devoid of infrastructure. The vertical resolution has been increased by developing borehole electrode arrays that can fit within the small-diameter drive casing of a direct push rig. The evolution of the program has led to some exceptional advances in the application of geophysical methods, including logistical deployment of the technology in hazardous areas, development of parallel processing resistivity inversion algorithms, and adapting the processing tools to accommodate electrodes of all shapes and locations. The program is accompanied by a full set of quality assurance procedures that cover the layout of sensors, measurement strategies, and software enhancements while insuring the integrity of stored data. The data have been shown to be useful in identifying previously unknown contaminant sources and defining the footprint of precipitation recharge barriers to retard the movement of existing contamination.     

综合遥感与地面观测的巢湖水体富营养化评价

提出一种将地面观测数据空间插值与遥感反演结合的湖泊富营养化评价方法与业务化运行模式。对叶绿素a等可反演参数利用遥感影像反演,并利用实测值校正获得高精度反演结果;对总磷等不易反演参数采用空间插值获取全湖区数据,采用综合营养指数法对巢湖富营养化状态进行反演,获得2015年5月12日巢湖富营养化状态空间分布情况。结果表明,巢湖全湖为轻度和中度富营养化状态,呈现出西半湖高于东半湖的总体空间分布趋势。结合相关数据对巢湖富营养化成因进行推断,认为南淝河等上游河流各类营养物质输入量较大是造成西半湖北部富营养化严重的主要成因;西南部杭埠河等河流氮磷输入量较大,但其他营养物质输入较少,使得该区域总体呈现出富营养化程度偏低的现象。     

Effects of urban sprawl on agricultural land: a case study of Kahramanmaraş, Turkey

The main objective of this study is to quantify areal loss of olive groves due to urban sprawl of the city of Kahramanmara?, Turkey. Spatial changes were analysed by interpreting the digitized data derived from a black–white monoscopic aerial photograph taken in 1985, panchromatic IKONOS image of 2000 and two pan-sharpened Quickbird images of 2004 and 2006. Data obtained revealed that the area of olive groves decreased by 25% from 460.55 ha in 1985 to 344.46 in 2006, while the number of parcels increased from 170 to 445. Of the total areal loss, 60% was due to building constructions, with the rest being due to clear-cut for new residential gardens composed of exotic plants, new buildings, or new roads. Rapid population growth, increased land prices due to urban expansion, and abandonment of agricultural practices to construction of multi-storey buildings were the main causes of the process that transformed the olive groves into urbanized areas. Results pointed to an urgent need to (1) revise the national and municipal land management practices, (2) balance the gap between the short- and long-term economic benefits that urban and community development plans ignore, and (3) monitor land-use changes periodically by using high resolution satellite images.     

Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach

Given the alarming global rates of mangrove forest loss it is important that resource managers have access to updated information regarding both the extent and condition of their mangrove forests. Mexican mangroves in particular have been identified as experiencing an exceptional high annual rate of loss. However, conflicting studies, using remote sensing techniques, of the current state of many of these forests may be hindering all efforts to conserve and manage what remains. Focusing on one such system, the Teacapán–Agua Brava–Las Haciendas estuarine–mangrove complex of the Mexican Pacific, an attempt was made to develop a rapid method of mapping the current condition of the mangroves based on estimated LAI. Specifically, using an AccuPAR LP-80 Ceptometer, 300 indirect in situ LAI measurements were taken at various sites within the black mangrove (Avicennia germinans) dominated forests of the northern section of this system. From this sample, 225 measurements were then used to develop linear regression models based on their relationship with corresponding values derived from QuickBird very high resolution optical satellite data. Specifically, regression analyses of the in situ LAI with both the normalized difference vegetation index (NDVI) and the simple ration (SR) vegetation index revealed significant positive relationships [LAI versus NDVI (R ² = 0.63); LAI versus SR (R ² = 0.68)]. Moreover, using the remaining sample, further examination of standard errors and of an F test of the residual variances indicated little difference between the two models. Based on the NDVI model, a map of estimated mangrove LAI was then created. Excluding the dead mangrove areas (i.e. LAI = 0), which represented 40% of the total 30.4 km² of mangrove area identified in the scene, a mean estimated LAI value of 2.71 was recorded. By grouping the healthy fringe mangrove with the healthy riverine mangrove and by grouping the dwarf mangrove together with the poor condition mangrove, mean estimated LAI values of 4.66 and 2.39 were calculated, respectively. Given that the former healthy group only represents 8% of the total mangrove area examined, it is concluded that this mangrove system, considered one of the most important of the Pacific coast of the Americas, is currently experiencing a considerable state of degradation. Furthermore, based on the results of this investigation it is suggested that this approach could provide resource managers and scientists alike with a very rapid and effective method for monitoring the state of remaining mangrove forests of the Mexican Pacific and, possibly, other areas of the tropics.     

Integrated groundwater resource management in Indus Basin using satellite gravimetry and physical modeling tools

Reliable and frequent information on groundwater behavior and dynamics is very important for effective groundwater resource management at appropriate spatial scales. This information is rarely available in developing countries and thus poses a challenge for groundwater managers. The in situ data and groundwater modeling tools are limited in their ability to cover large domains. Remote sensing technology can now be used to continuously collect information on hydrological cycle in a cost-effective way. This study evaluates the effectiveness of a remote sensing integrated physical modeling approach for groundwater management in Indus Basin. The Gravity Recovery and Climate Experiment Satellite (GRACE)-based gravity anomalies from 2003 to 2010 were processed to generate monthly groundwater storage changes using the Variable Infiltration Capacity (VIC) hydrologic model. The groundwater storage is the key parameter of interest for groundwater resource management. The spatial and temporal patterns in groundwater storage (GWS) are useful for devising the appropriate groundwater management strategies. GRACE-estimated GWS information with large-scale coverage is valuable for basin-scale monitoring and decision making. This frequently available information is found useful for the identification of groundwater recharge areas, groundwater storage depletion, and pinpointing of the areas where groundwater sustainability is at risk. The GWS anomalies were found to favorably agree with groundwater model simulations from Visual MODFLOW and in situ data. Mostly, a moderate to severe GWS depletion is observed causing a vulnerable situation to the sustainability of this groundwater resource. For the sustainable groundwater management, the region needs to implement groundwater policies and adopt water conservation techniques.     

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

A study was conducted in central Texas to determine the potential of using remote sensing technology to distinguish Ashe juniper (Juniperus ashei Buchholz) infestations on rangelands. Plant canopy reflectance measurements showed that Ashe juniper had lower near-infrared reflectance than other associated woody plant species and lower visible reflectance than mixed herbaceous species in spring and summer. Ashe juniper could be distinguished on color-infrared aerial photographs acquired in March, April, June, and August and on QuickBird false color satellite imagery obtained in June, where it had a distinct dark reddish-brown tonal response. Unsupervised classification techniques were used to classify aerial photographic and satellite imagery of study sites. An accuracy assessment performed on a computer classified map of a photographic image showed that Ashe juniper had producer's and user's accuracies of 100% and 92.9%, respectively, whereas an accuracy assessment performed on a classified map of a satellite image of the same site showed that Ashe juniper had producer's and user's accuracies of 94.1% and 88.1%, respectively. Accuracy assessments performed on classified maps of satellite images of two additional study sites showed that Ashe juniper had producer's and user's accuracies that ranged from 87.1% to 96.4%. These results indicate that both color-infrared photography and false color satellite imagery can be used successfully for distinguishing Ashe juniper infestations.     

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.