Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

This study aims at the classification and water quality assessment of Harike wetland (Ramsar site) in India using satellite images from the Indian Remote Sensing satellite, Resourcesat (IRS P6). The Harike wetland is a converging zone of two rivers, Beas and Sutlej. The satellite images of IRS Linear Imaging Self Scanner (LISS) IV multispectral sensor with three bands (green, red, and near infrared (NIR)) and a spatial resolution of 5.8 m were classified using supervised image classification techniques. Field points for image classification and water sampling were recorded using a Garmin eTrex Global Positioning System. The water quality parameters assessed were dissolved oxygen, conductivity, pH, turbidity, total and suspended solids (SS), chemical oxygen demand, and Secchi disk transparency (SDT). Correlations were established between turbidity and SS, SS and SDT, and total solids and turbidity. Using reflectance values from the green, red, and NIR bands, we then plotted the water quality parameters with the mean digital number values from the satellite imagery. The NIR band correlated significantly with the water quality parameters, whereas, using SDT values, it was observed that the green and the red reflectance bands were able to distinguish the waters from the two rivers, which have different water qualities.     

Pond bank access as an approach for managing toxic cyanobacteria in beef cattle pasture drinking water ponds

Forty-one livestock drinking water ponds in Alabama beef cattle pastures during were surveyed during the late summer to generally understand water quality patterns in these important water resources. Since livestock drinking water ponds are prone to excess nutrients that typically lead to eutrophication, which can promote blooms of toxigenic phytoplankton such as cyanobacteria, we also assessed the threat of exposure to the hepatotoxin, microcystin. Eighty percent of the ponds studied contained measurable microcystin, while three of these ponds had concentrations above human drinking water thresholds set by the US Environmental Protection Agency (i.e., 0.3 μg/L). Water quality patterns in the livestock drinking water ponds contrasted sharply with patterns typically observed for temperate freshwater lakes and reservoirs. Namely, we found several non-linear relationships between phytoplankton abundance (measured as chlorophyll) and nutrients or total suspended solids. Livestock had direct access to all the study ponds. Consequently, the proportion of inorganic suspended solids (e.g., sediment) increased with higher concentrations of total suspended solids, which underlies these patterns. Unimodal relationships were also observed between microcystin and phytoplankton abundance or nutrients. Euglenoids were abundant in the four ponds with chlorophyll concentrations >?250 μg/L (and dominated three of these ponds), which could explain why ponds with high chlorophyll concentrations would have low microcystin concentrations. Based on observations made during sampling events and available water quality data, livestock-mediated bioturbation is causing elevated total suspended solids that lead to reduced phytoplankton abundance and microcystin despite high concentrations of nutrients, such as phosphorus and nitrogen. Thus, livestock could be used to manage algal blooms, including toxic secondary metabolites, in their drinking water ponds by allowing them to walk in the ponds to increase turbidity.     

Shadow attenuation with high dynamic range images

Shadow often interferes with accurate image analysis. To mitigate shadow effects in near-earth imagery (2 m above ground level), we created high dynamic range (HDR) nadir images and used them to measure grassland ground cover. HDR composites were created by merging three differentially exposed images spanning a wide exposure range and resulted in lightened shadows. HDR images showed more detail; reduced the numbers of pure black, pure white, and pixels visually indistinguishable from black and white; reapportioned skewed luma values towards a normal distribution; and increased the Euclidean distance between litter and bare ground RGB values--allowing increased feature separation; all of which facilitated an increase in real feature classification through manual image analysis. Drawbacks to the method included decreased image sharpness due to minor misalignment of images or moving vegetation, time required to create HDR images, and difficulty with acquiring primary images from a moving platform. We conclude that HDR imagery can provide more accurate measurements of bare soil cover for ecosystem monitoring and assessment.     

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.     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

Montane Meadows as Indicators of Environmental Change

We used a time series of satellite multispectral imagery for mapping and monitoring six classes of montane meadows arrayed along a moisture gradient (from hydric to mesic to xeric). We hypothesized that mesic meadows would support the highest species diversity of plants, birds, and butterflies because they are more moderate environments. We also hypothesized that mesic meadows would exhibit the greatest seasonal and interannual variability in spectral response across years. Field sampling in each of the meadow types was conducted for plants, birds, and butterflies in 1997 and 1998. Mesic meadows supported the highest plant species diversity, but there was no significant difference in bird or butterfly species diversity among meadow types. These data show that it may be easier to detect significant differences in more species rich taxa (e.g., plants) than taxa that are represented by fewer species (e.g., butterflies and birds). Mesic meadows also showed the greatest seasonal and interannual variability in spectral response. Given the rich biodiversity of mesic montane meadows and their sensitivity to variations in temperature and moisture, they may be important to monitor in the context of environmental change     

Applying Satellite Imagery to Triage Assessment of Ecosystem Health

Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR) imagery to develop calibration patterns of change in the Normalized Difference Vegetation Index (NDVI) over the growing season for selected sites for which we had ground data and historical data characterizing these sites as irreversibly degraded. We used the NDVI curves for these training sites to classify and map the irreversibly degraded rangelands in southern New Mexico. We composited images into four year blocks: 1988–1991, 1989–1992, and 1990–1993. The overlap in pixels classified as irreversibly degraded ranged from 42.6% to 84.3% in year block comparisons. Quantitative data on vegetation composition and cover were collected at 13 sites within a small portion of the study area. Wide coverage reconnaissance of boundaries between vegetation types was also conducted for comparisons with year block maps. The year block 1988–1991 provided the most accurate delineation of degraded areas. The rangelands of southern New Mexico experienced above average precipitation from 1990–1993. The above average precipitation resulted in spatially variable productivity of ephemeral weedy plants on the training sites and degraded rangelands which resulted in much smaller areas classified as irreversibly degraded. We selected imagery for a single year, 1989, which was characterized by the absence of spring annual plant production in order to eliminate the confounding effect of reflectance from annual weeds. That image analysis classified more than 20% of the rangelands as irreversibly degraded because areas with shrub-grass mosaic were included in the degraded classification. The single year image included more than double the area classified as irreversibly degraded by the year blocks. AVHRR imagery can be used to make triage assessments of irreversibly degraded rangeland but such assessment requires understanding productivity patterns and variability across the landscapes of the region and careful selection of the years from which imagery is chosen.     

Trophic state index of a lake system using IRS (P6-LISS III) satellite imagery

Water pollution has now become a major threat to the existence of living beings and water quality monitoring is an effective step towards the restoration of water quality. Lakes are versatile ecosystems and their eutrophication is a serious problem. Carlson Trophic State Index (CTSI) provides an insight into the trophic condition of a lake. CTSI has been modified for the study area and is used in this study. Satellite imagery analysis now plays a prominent role in the quick assessment of water quality in a vast area. This study is an attempt to assess the trophic state index based on secchi disk depth and chlorophyll a of a lake system (Akkulam?CVeli lake, Kerala, India) using Indian Remote Sensing (IRS) P6 LISS III imagery. Field data were collected on the date of the overpass of the satellite. Multiple regression equation is found to yield superior results than the simple regression equations using spectral ratios and radiance from the individual bands, for the prediction of trophic state index from satellite imagery. The trophic state index based on secchi disk depth, derived from the satellite imagery, provides an accurate prediction of the trophic status of the lake. IRS P6-LISS III imagery can be effectively used for the assessment of the trophic condition of a lake system.     

Determination of chlorophyll-a amount in Golden Horn, Istanbul, Turkey using IKONOS and in situ data

The objective of this research was to explore an accurate and fast way of estimating chlorophyll-a amount, a water quality parameter (WQP), using IKONOS satellite sensor image and in situ measurements. Since the in situ data of WQPs are limited with the number of sampling locations, deriving a correlation between these measurements and remotely sensed image allows synoptic estimates of the related parameter over large areas even if the areas are in remote and inaccessible locations. In this study, simultaneously collected satellite image data and in situ measurements of chlorophyll-a were correlated using multivariate regression model. Different experiments were designed by changing the numbers and distributions of in situ measurements. Regression coefficients of each design and differences between model-derived data and in situ measurements were calculated to find out the optimum design to produce chlorophyll-a map of study region. Results illustrated that both the number and distribution of in situ measurements have impact on regression analysis, therefore should be selected attentively. Also, it is found that IKONOS imagery is an efficient and effective source to derive chlorophyll-a map of the large areas using limited number of ground measurements.     

湖泊重要入湖河流稳定水像元提取技术

利用2010年ALOS影像研究了归一化水体指数(NDWI)在两种极端情况下,即像元四边与河岸线最小夹角分别为0°和45°,与离岸距离之间的关系,提出了获得稳定水像元的最小河宽,并以太湖6条重要入湖河流(西苕溪、望虞河、太浦河、乌溪港、梁溪-东清河、武进港)为例,研究了稳定水像元的分布。结果表明,NDWI与离岸距离之间的关系可用Logistic方程进行拟合,两种极端情况的p值均小于0.01;最小夹角为0°和45°的情况下,获得稳定水像元的最小河宽分别为96 m和88 m,即当河宽大于96 m时,可在ALOS影像上获得稳定的水像元;6条入湖河流可获得稳定水像元的长度占总长的4.59%~100%,难以获取稳定水像元的最长距离为0~18.55 km。     

Telemetric System for Hydrology and Water Quality Monitoring in Watersheds of Northern New Mexico, Usa

This study utilized telemetric systems to sample microbes and pathogens in forest, burned forest, rangeland, and urban watersheds to assess surface water quality in northern New Mexico. Four sites included remote mountainous watersheds, prairie rangelands, and a small urban area. The telemetric system was linked to dataloggers with automated event monitoring equipment to monitor discharge, turbidity, electrical conductivity, water temperature, and rainfall during base flow and storm events. Site data stored in dataloggers was uploaded to one of three types of telemetry: 1) radio in rangeland and urban settings; 2) a conventional phone/modem system with a modem positioned at the urban/forest interface; and 3) a satellite system used in a remote mountainous burned forest watershed. The major variables affecting selection of each system were site access, distance, technology, and cost. The systems were compared based on operation and cost. Utilization of telecommunications systems in this varied geographic area facilitated the gathering of hydrologic and water quality data on a timely basis.     

An integrated approach for spatio-temporal variability analysis of wetlands: a case study of Abaya and Chamo lakes, Ethiopia

Starting with the intensification of irrigation activities in the beginning of 1980s in Abaya and Chamo lakes area, the decreasing water inflow to the lakes caused denudation of the wetlands. The ecological situation in the lake region changed significantly during last four decades. The lakes and associated wetlands change have been studied using Landsat MSS (1973), Landsat TM (1986), and Ladsat ETM (2000) satellite imagery. Along with satellite imagery, other hydro-meteorological data were collected and hydro-meteorological data analyses were done to assess the variability of wetlands. From these data, lakes morphometric property estimation at different time series and water balance analysis for both lakes were done. Wetlands are mapped from the TCT image and these maps are subject to change detection to see the temporal and spatial variability of the wetlands. Moreover, the lake-morphometric area and volume variation have been studied. The result showed that between 1986 and 2000, a significant reduction has been observed but lesser than the previous decades (6.4 km(2)). The identified reason behind this change is that the free settlement and shoreline cultivation of the wetlands causing the soil erosion and eventually adds the sediment to the wetlands.     

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches—object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)—for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25?×?25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50?×?50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.     

Assessment of Wildfire Hazards with a Semiparametric Spatial Approach

Rural households in agricultural economies are vulnerable to several environmental risks such as fires, floods, and droughts that may affect their productivity in whole or in part. These hazards are especially relevant in developing countries where farmers have few or no access to traditional risk-transfer techniques, such as insurance and finance, and where low governmental investments in rural infrastructure, risk assessment techniques, or early warning systems makes the aftermath of such hazards more expensive and results in slower recovery for those who are affected. In this paper, we use historical satellite data (Terra) of burned areas in South America to fit a semiparametric spatial model, based on kernel smoothing and on a nonlinear relationship between average time between events and damage, to assess the environmental hazard affecting the land’s productivity. The results were twofold: first, we were able to develop a spatial assessment of fire hazard, and second, we were able to evaluate how much a farmer may lose in terms of productivity per hectare due to the environmental hazard. The methodology may be easily adapted to other world regions; to different environmental hazards such as floods, windbreak, windthrow, or related land-use changes; or to integrate various environmental hazards simultaneously, as long as they can be monitored via remote sensing (e.g., satellite imagery, aerial photographs, etc).     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

This study compared performance of four change detection algorithms with six vegetation indices derived from pre- and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in order to select an optimal remote sensing technique for identifying forestlands disturbed by Hurricane Katrina. The algorithms included univariate image differencing (UID), selective principal component analysis (PCA), change vector analysis (CVA), and postclassification comparison (PCC). The indices consisted of near-infrared to red ratios, normalized difference vegetation index, Tasseled Cap index of greenness, brightness, and wetness (TCW), and soil-adjusted vegetation index. In addition to the satellite imagery, the “ground truth” data of forest damage were also collected through field investigation and interpretation of post-Katrina aerial photos. Disturbed forests were identified by classifying the composite and the continuous change imagery with the supervised classification method. Results showed that the change detection techniques exerted apparent influence on detection results with an overall accuracy varying between 51% and 86% and a kappa statistics ranging from 0.02 to 0.72. Detected areas of disturbed forestlands were noticeable in two groups: 180,832–264,617 and 85,861–124,205 ha. The landscape of disturbed forests also displayed two unique patterns, depending upon the area group. The PCC algorithm along with the composite image contributed the highest accuracy and lowest error (0.5%) in estimating areas of disturbed forestlands. Both UID and CVA performed similarly, but caution should be taken when using selective PCA in detecting hurricane disturbance to forests. Among the six indices, TCW outperformed the other indices owing to its maximum sensitivity to forest modification. This study suggested that compared with the detection algorithms, proper selection of vegetation indices was more critical for obtaining satisfactory results.     

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.     

Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

Forest disturbances around the world have the potential to alter forest type and cover, with impacts on diversity, carbon storage, and landscape composition. These disturbances, especially fire, are common and often large, making ground investigation of forest recovery difficult. Remote sensing offers a means to monitor forest recovery in real time, over the entire landscape. Typically, recovery monitoring via remote sensing consists of measuring vegetation indices (e.g., NDVI) or index-derived metrics, with the assumption that recovery in NDVI (for example) is a meaningful measure of ecosystem recovery. This study tests that assumption using MODIS 16-day imagery from 2000 to 2010 in the area of the Colorado’s Routt National Forest Hinman burn (2002) and seedling density counts taken in the same area. Results indicate that NDVI is rarely correlated with forest recovery, and is dominated by annual and perennial forb cover, although topography complicates analysis. Utility of NDVI as a means to delineate areas of recovery or non-recovery are in doubt, as bootstrapped analysis indicates distinguishing power only slightly better than random. NDVI in revegetation analyses should carefully consider the ecology and seasonal patterns of the system in question.     

Comprehensive monitoring of drinking well water quality in Seoul metropolitan city, Korea

In this research, the quality of drinking well waters from 14 districts around Seoul metropolitan city, Korea was assessed by measuring a number of parameters with established guideline (e.g., arsenic, fluoride, nitrate nitrogen, benzene, 1,2-dichloroethene, dichloromethane, copper, and lead) and without such criteria (e.g., hardness, chloride ion, sulfate ion, ammonia nitrogen, aluminum, iron, manganese, and zinc). Physical parameters such as evaporation residue (or total dissolved solids) and turbidity were also measured. The importance of each parameter in well waters was examined in terms of the magnitude and exceedance frequency of guideline values established by international (and national) health agencies. The results of this study indicate that among the eight parameters with well-established guidelines (e.g., WHO), arsenic and lead (guideline value of 0.01 mg?L^?1 for both) recorded the highest exceedance frequency of 18 and 16 well samples ranging in 0.06–136 and 2–9 mg?L^?1, respectively. As such, a number of water quality parameters measured from many well waters in this urban area were in critical levels which require immediate attention for treatment and continuous monitoring.