Thermal terrain modeling of spatial objects, a tool for environmental and climatic change assessment

The aim of the current research effort is to include biophysical multi-temporal data and more specifically land surface temperature (LST) in the terrain modeling process that traditionally was based only on digital elevation data processing. The terrain partition framework (spatial objects) is defined by the borderlines of prefecture authorities of Greece. Each object is represented by a set of attributes derived from the digital elevation data, and objects are organized into clusters on the basis of their terrain dependent representation. Finally, the terrain is segmented to regions on the basis of the multi-temporal LST data, each region presenting a different thermal signature. The thermal regions are used in the spatial objects parametric representation and a new index is devised (LST climatic index) expressing the biophysical suitability of spatial objects at moderate resolution scale.     

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.     

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.     

Atmospheric influences on water quality: a simulation of nutrient loading for the Pearl River Basin, USA

Knowledge of water quality conditions is essential in assessing the health of riverine ecosystems. The goal of this study is to determine the degree to which water quality variables are related to precipitation and air temperature conditions for a segment of the Pearl River Basin near Bogalusa, LA, USA. The AQUATOX ecological fate simulation model is used to estimate daily total nitrogen, total phosphorus, and dissolved oxygen concentrations over a 2-year period. Daily modeled output for each variable was calibrated against reliably measured data to assess the accuracy. Observed data were plotted against simulated data for controlled and perturbed models for validation, and stepwise multiple regression analysis was used to quantify the relationships between the water quality and meteorological variables. Results suggest that daily dissolved oxygen is significantly negatively correlated to concurrent daily mean air temperature with a total explained variance of 0.679 (p?<?0.01), and monthly dissolved oxygen is significantly negatively correlated to monthly mean air temperature with a total explained variance of 0.567 (p?<?0.01). Total mean monthly phosphorus concentration is significantly positively related to the previous month's precipitation with a total explained variance of 0.302 (p?<?0.01). These relationships suggest that atmospheric conditions have a strong influence on water quality in the Pearl Basin. Therefore, environmental planners should expect that future climatic changes are likely to alter water quality.     

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.     

Assessing the effects of dam building on land degradation in central Iran with Landsat LST and LULC time series

This study aimed to analyze the impact of Zayandehrood Dam on desertification using the spatio-temporal dynamics of land use/land cover (LULC) and land surface temperature (LST) in an arid environment in central Iran from 1987 to 2014. The LULC and LST images were calculated from Landsat TM, ETM+, and OLI data, and their accuracies were assessed against reference data using error matrix and linear regression analysis. Results showed that salty and bare lands increased up to 57,302 ha, while agricultural lands declined substantially (28,275.58 ha) in the region. The changes in LULC classes resulted in dramatic variations in LST values. The average temperature showed a 5.03 °C increase, and the minimum temperature increased by 5.66 °C. LST had an increasing trend in bare lands (8.74 °C), poor rangelands (6.8 °C), agricultural lands (9.46 °C), salty lands (9.6 °C), and residential areas (3.18 °C) in this 27-year period. Rainfall and temperature trend analysis revealed that the main cause of these extreme changes in LULC and LST was largely attributed to the drying up of Zayandehrood River due to dam construction and allocating water mainly for industrial sectors. Results indicate that in addition to LULC changes, the spatio-temporal variations of LST can be used as an effective index in desertification assessment and monitoring in arid environments.     

Evaluation of dispersal volcanic products of recent events in lichens in environmental gradient,Nahuel Huapi National Park,Argentina

The atmospheric transport of volcanic products are subject to several variables, mainly the height of the eruption column and wind direction, thus elements associated with the ashes are deposited in major or lesser degree depending on variables as latitude, wind and humidity. The lichens are able to reflect the atmospheric fallout. The present work evaluated the correlation between meteorological parameters, geographic locations, sulphur and other element concentrations in lichens genus Usnea affected by Puyehue–Cordón Caulle complex (North Patagonia Andean Range) eruption of June 4, 2011. Semiquantitative analyses of biological elements by scanning electron microscope methods, sulphur (S) by LECO and other elements by instrumental neutron activation were evaluated by principal component analysis. Elements as antimony, arsenic, barium, bromine, calcium, caesium, potassium, rubidium, selenium, and uranium correlated with distance to volcano, also calcium and potassium with longitude while bromine, rubidium, and potassium with humidity. Those results indicate that Usnea sp. is a good bioindicator of the atmospheric volcanic emissions in relation to environmental gradient.     

An Approach to Reaeration Coefficient Modeling in Local Surface Water Quality Monitoring

Reaeration coefficient (k ₂) for River Atuwara, Ogun State, Nigeria was calculated from dissolved oxygen and biochemical oxygen demand data collected over period of 3 months covering the two prevailing climatic seasons in the country. Both the Akaike and Bayesian information criteria were used in the selection and analysis of ten models to identify the most suitable reaeration coefficient (k ₂) model for Atuwara River. Models that passed the confidence limit were subjected to model evaluation using measures of agreement between observed and predicted data such as percent bias, Nash–Sutcliffe efficiency, and root mean square observation standard deviation ratio. The used approach yield better results than empirical models developed for local conditions while it is also useful in conserving scarce resources.     

Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

Mapping spatial distribution of climatological parameters with a good degree of accuracy is crucial in environmental modeling and planning. Nowadays, there are various models to estimate and predict spatial variables in an area but some such as cokriging and geographically weighted regression (GWR) have got more attention from experts in this field. The objectives of this study are to evaluate and compare GWR with ordinary cokriging (OCK) techniques for estimating the mean annual air temperature (MAT) of Iran using European Centre for Medium-Range Weather Forecasts (ECMWF) data and auxiliary variables (e.g., longitude, latitude and altitude). The MAT-gridded data for Iran was collected in pixels during the time interval of 1987–2015 from the ERA-Interim re-analysis version of ECMWF. Validation results indicate that cokriging model with latitude and altitude for estimating MAT has the lowest MAE (0.0155), MBE (0.00085), RMSE (0.0251), and the highest NS (0.9999) in relation to other cokriging methods. On the other hand, GWR with altitude has better results than those of GWR with other auxiliary variables because of its MAE (0.1271), MBE (0.0124), RMSE (0.1760), and NS (0.9969). By comparing two mentioned methods, cokriging with latitude and altitude has provided the best performance in relation to GWR for prediction of MAT in Iran. To obtain accurate estimation of the spatial distribution of MAT, local residuals were analyzed. Results concluded that residuals of the OCK model have high spatial adaptations between the observed and predicted MAT data compared to the GWR model. Hence, OCK was a relatively optimum method for the estimation of MAT compared with GWR.

     

Species Distribution Models of Freshwater Stream Fishes in Maryland and Their Implications for Management

Species distribution models (SDMs) are often used in conservation planning, but their utility can be improved by assessing the relationships between environmental and species response variables. We constructed SDMs for 30 stream fishes of Maryland, USA, using watershed attributes as environmental variables and presence/absence as species responses. SDMs showed substantial agreement between observed and predicted values for 17 species. Most important variables were natural attributes (e.g., ecoregion, watershed area, latitude/longitude); land cover (e.g., %impervious, %row crop) was important for three species. Focused analyses on four representative species (central stoneroller, creek chub, largemouth bass, and white sucker) showed the probability of presence of each species increased non-linearly with watershed area. For these species, SDMs built to predict absent, low, and high densities were similar to presence/absence predictions but provided probable locations of high densities (e.g., probability of high-density creek chub decreased rapidly with watershed area). We applied SDMs to predict suitability of watersheds within the study area for each species. Maps of suitability and the environmental and species response relationships can help develop better management plans.     

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.     

Dynamic Modeling of Biological Treatment of Leachates from Solid Wastes

Leachates from municipal solid wastes are liquids formed by organic and inorganic components suitable for biological treatment. In biological treatment plants, prediction of the output parameter value due to dynamic variation is essential. A dynamic model based on ASM1 has been applied for prediction of effluent COD value at real scale, using three different composition leachates. Tuning between dynamic variables and predicted parameter is tested for a correct proposal of the model. Dynamic model predicts correctly fluctuations in the output COD value and tempers dynamism caused by variation of temperature and biomass concentration in the case of high organic loaded leachates. Kinetic parameters Y _H and K _s are fixed to solve the model, and dynamic variables μ _H and X_BH are changing in time. μ_H,max is obtained in simulation (0.37–1.82 day^?1) for minimum error between predicted values and experimental data. μ_H,max value is much lower compared with the value for domestic wastewater treatment due to lower biodegradability of leachate.     

A geographical approach to tracking Escherichia coli and other water quality constituents in a Texas coastal plains watershed

Diffuse sources of surface water pathogens and nutrients can be difficult to isolate in larger river basins. This study used a geographical or nested approach to isolate diffuse sources of Escherichia coli and other water quality constituents in a 145.7-km² river basin in south central Texas, USA. Average numbers of E. coli ranged from 49 to 64,000 colony forming units (CFU) per 100 mL depending upon season and stream flow over the 1-year sampling period. Nitrate-N concentrations ranged from 48 to 14,041 μg?L^?1 and orthophosphate-P from 27 to 2,721 μg?L^?1. High concentrations of nitrate-N, dissolved organic nitrogen, and orthophosphate-P were observed downstream of waste water treatment plants but E. coli values were higher in a watershed draining an older part of the city. Total urban land use explained between 56 and 72 % of the variance in mean annual E. coli values (p?<?0.05) in nine hydrologically disconnected creeks. Of the types of urban land use, commercial land use explained most of the variance in E. coli values in the fall and winter. Surface water sodium, alkalinity, and potassium concentrations in surface water were best described by the proportion of commercial land use in the watershed. Based on our nested approach in examining surface water, city officials are able to direct funding to specific areas of the basin in order to mitigate high surface water E. coli numbers and nutrient concentrations.     

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.     

Nitrogen Content of Letharia vulpina Tissue from Forests of the Sierra Nevada, California: Geographic Patterns and Relationships to Ammonia Estimates and Climate

Nitrogen (N) pollution is a growing concern in forests of the greater Sierra Nevada, which lie downwind of the highly populated and agricultural Central Valley. Nitrogen content of Letharia vulpina tissue was analyzed from 38 sites using total Kjeldahl analysis to provide a preliminary assessment of N deposition patterns. Collections were co-located with plots where epiphytic macrolichen communities are used for estimating ammonia (NH₃) deposition. Tissue N ranged from 0.6% to 2.11% with the highest values occurring in the southwestern Sierra Nevada (range: 1.38 to 2.11). Tissue N at 17 plots was elevated, as defined by a threshold concentration of 1.03%. Stepwise regression was used to determine the best predictors of tissue N from among a variety of environmental variables. The best model consisted only of longitude (r ² = 0.64), which was reflected in the geographic distribution of tissue values: the southwestern Sierra Nevada, the high Sierras near the Tahoe Basin, and the Modoc Plateau, are three apparent N hotspots arranged along the tilted north–south axis of the study area. Withholding longitude and latitude, the best regression model suggested that NH₃ estimates and annual number of wetdays interactively affect N accumulation (r ² = 0.61; % N ∼ NH₃ + wetdays + (NH₃ × wetdays)). We did not expect perfect correspondence between tissue values and NH₃ estimates since other N pollutants also accumulate in the lichen thallus. Additionally, other factors potentially affecting N content, such as growth rate and leaching, were not given full account.     

利用GE Plus快速解译土地利用类型和辅助制图

GE Plus超强版比GE普通版具有更强的空间编辑功能。本文介绍了一种利用GE Plus超强版软件,无需使用ArcGIS等专用地理信息软件和无需购买高分辨率遥感影像实现区域土地利用类型快速解译和辅助制图的方法。该方法主要使用GE中工具菜单、添加菜单和标尺工具栏完成区域土地利用类型快速解译和辅助制图。本文以乌鲁木齐市某社区为例,成功完成了案例区基于高分辨率遥感影像的土地利用类型信息提取和快速制图,并附经纬度和比例尺,可为开展建设项目工业场地类环境影响评价和建设项目竣工环保验收监测(调查)工作提供基础数据和背景资料。     

Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites

This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites’ land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.     

Compositing climate change vulnerability of a Mediterranean region using spatiotemporally dynamic proxies for ecological and socioeconomic impacts and stabilities

The study presents a new methodology to quantify spatiotemporal dynamics of climate change vulnerability at a regional scale adopting a new conceptual model of vulnerability as a function of climate change impacts, ecological stability, and socioeconomic stability. Spatiotemporal trends of equally weighted proxy variables for the three vulnerability components were generated to develop a composite climate change vulnerability index (CCVI) for a Mediterranean region of Turkey combining Landsat time series data, digital elevation model (DEM)-derived data, ordinary kriging, and geographical information system. Climate change impact was based on spatiotemporal trends of August land surface temperature (LST) between 1987 and 2016. Ecological stability was based on DEM, slope, aspect, and spatiotemporal trends of normalized difference vegetation index (NDVI), while socioeconomic stability was quantified as a function of spatiotemporal trends of land cover, population density, per capita gross domestic product, and illiteracy. The zones ranked on the five classes of no-to-extreme vulnerability were identified where highly and moderately vulnerable lands covered 0.02% (12 km²) and 11.8% (6374 km²) of the study region, respectively, mostly occurring in the interior central part. The adoption of this composite CCVI approach is expected to lead to spatiotemporally dynamic policy recommendations towards sustainability and tailor preventive and mitigative measures to locally specific characteristics of coupled ecological–socioeconomic systems.     

Noise tolerance of algorithms for estimating chlorophyll a concentration in turbid waters

The accuracy and noise tolerance of 13 global models and 5 Case II chlorophyll a (chl a) retrieval models were evaluated using three dataset. It was found that if 5 % input noise related to atmospheric correction is considered, then the uncertainty associated with noise tolerance varied from 5.5 % to 55.6 %, and these uncertainties generally accounts for 15.63 % to 24.75 % of the total uncertainty. This observation suggests that an optimal algorithm not only should have a strong chl a concentration prediction ability but also should possess high insensitivity to the noise of remote-sensing imagery. The accuracy evaluations of chl a models were based on comparisons of chl a predicted models with chl a concentration measured analytically for field measurements. The results indicate that none of the selected chl a estimation algorithms provide accurate retrievals of chl a in turbid waters. This may be attributed to the strong optical influence of organic and inorganic matter at the blue green range, and the non-negligible of non-organic matter absorption at the red and near-infrared ranges. In order to solve this problem, the chl a concentration retrieval models must be further optimized. After being optimized using the empirical optimized method constructed in this paper, a single parameterized NDCI (normalized difference chl a index) model produces accurate retrievals in the Yellow River Estuary, Taihu Lake and Chesapeake Bay. If 5 % input noise associated with residual uncertainty 0of atmospheric correction is taken into account, the model produces only 29.96 % uncertainty for the remote sensing of chl a concentration in these three turbid waters.