Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.     

Assessment of the mutual impact between climate and vegetation cover using NOAA-AVHRR and Landsat data in Egypt

The objective of the current study is to use satellite data to assess the mutual influence between vegetation and climate. The Ismailia Governorate was selected as a case study to investigate the impact of vegetation cover expansion on both land surface and air temperature from 1983 to 2010 and vice versa. This observation site was carefully selected as a clear example of the high rate of the reclamation and vegetation expansion process in Egypt. Land surface temperature (LST) was estimated through the Advanced Very High Resolution Radiometer (a space-borne sensor embarked on the National Oceanic and Atmospheric Administration) data while air temperature (T _air) was collected from ground meteorological stations in the study area. Irrigated agriculture is the largest consumer of freshwater resources. However, consistent information on irrigation water use is still lacking. Relative humidity, wind speed, solar radiation, and T _air data were inserted in the Penman–Monteith equation to calculate potential evapotranspiration (ET_o), while both LST and T _air were used to observe the relative water status of the study area as a result of the water deficit index (WDI). Then, both WDI and ET_o were used to calculate actual evepotranspiration (ET_C.). The results showed that LST decreased by about 2.3 °C while T _air decreased by about 1.6 °C during the study period. The results showed also that the vegetation cover expanded from 25,529.85 ha in 1985 to 63,140.49 ha in 2009 with about 147 % increase. This decrease in LST and air temperature was according to the expansion of the cultivated land that was proved through the processing of three Landsat TM and Landsat ETM+ imageries acquired in June 19, 1985, June 7, 1998, and June 29, 2009. The vegetation water consumption was affected by the decreasing surface and air temperature. The results showed that the water deficit index decreased by about 0.35, and actual evapotranspiration increased by about 2.5 mm during the study period.     

Analysing transition of land surface temperature and derived indices with respect to elevation values in Kohima Saddar

Human activities have affected the urban environment resulting in a drastic change in the surface temperature. The impact of urban heat islands is noticeable in urban areas than in rural areas. The thermal band of Landsat 8 data is used to retrieve the spatial distribution of land surface temperature (LST) over Kohima Sadar for the years 2009, 2015 and 2020 with the Mono-window algorithm. Urban Thermal Field Variance Index (UTFVI) is used to assess the ecological condition in the area impacted by LST. Cartosat-1 Digital Elevation Model (Carto DEM) is used to understand the variations of LST and indices values with reference to the elevation profile located at different random points. The variations in the land cover are categorized as per the values of normalized difference vegetation index (NDVI) and built-up density index (BUI). This work estimates the influence of elevation over LST, vegetation, and the built-up area. Results implies a negative correlation between LST and NDVI whereas a positive correlation between LST and BUI. Likewise, NDVI and BUI show a strong negative correlation. It is observed that LST is independent of elevation profile but the variation of LST depends on the impact of change in topography urbanization, deforestation, and afforestation. There is no significant relationship of elevation with the variations in NDVI and BUI values. It is observed that the impact of emissivity influences the estimation of LST values. For the locations having the highest and lowest LST, NDVI, and BUI values, 50 random points are generated for the entire region, and validation is executed with the google earth historical image.

     

Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.     

Multi-perspective analysis of vegetation cover changes and driving factors of long time series based on climate and terrain data in Hanjiang River Basin,China

The Hanjiang River Basin is the source area of the Middle Route Project of the South-to-North Water Diversion Project, and the vegetation coverage in this basin directly affects the quality of the ecological environment. This study is based on long time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data synthesized over 16 days from 2000 to 2016 in the Hanjiang River Basin. Major climatic data (temperature and rainfall) and topographic data (elevation, slope, and aspect) are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: for the 2000–2016 period, the average annual NDVI is 0.823, with a change trend of 0.025 year^?1. The overall NDVI upstream is higher than that downstream. The average annual value of NDVI upstream is 0.844, with a change trend of 0.036 year^?1, and that of downstream is 0.799, with a change trend of 0.022 year^?1. The spatial distribution of NDVI was significantly increased in the area around the upstream section of the river and near the Danjiangkou Reservoir, and the distribution of NDVI around the central city was significantly reduced. The NDVI was positively correlated with temperature and rainfall, and the impacts differed among different regions. At elevations below 2000 m, the NDVI shows an increasing trend with increasing elevation, and at elevations exceeding 2000 m, the NDVI is negatively correlated with elevation. Slope is positively correlated with the NDVI. The influence of aspect on the NDVI was small.     

Iterative selective spatial variance reduction of MYD11A2 LST data

The aim of this research effort is to develop a method that will allow to map and evaluate thermal anomalies in SW USA from the MYD11A2 night land surface temperature (LST) imagery being available for the year 2014, that present higher spatial (1 km) and temporal (46 images per year) resolution than the MYD11C3 LST data (12 images per year at 5.6 km spatial resolution). The fact that is MYD11A2 LST imagery is projected to a rectangular grid did not affect the X, Y and elevation (H) spatial decorrelation stretch. Principal component analysis and linear regression models isolated and removed the X, Y, H (spatial) dependent variance included in the data while metrics devised verified the selective spatial variance reduction. The reconstructed 46 LST images represent the amount the LST deviates from the X, Y and H predicted for the year 2014. The thematic information content of the reconstructed LST images is verified by cluster analysis and mapped the spatial extend and the temporal variability of thermal anomalies within the study area. The positive thermal anomaly clusters are spatially arranged mainly west of Sierra Nevada in Great Basin Section where extensional tectonics create a series of titled elongated mountain blocks along the N to S direction in between basins bounded by normal faults, while the negative thermal anomaly clusters are spatially arranged along the coastal region, further north and in the western region far from the tilted mountain tectonic blocks of the Great Basin Section. The spatial maps that define regions with (positive or negative) thermal anomalies and distinct mean land response could assist landcover studies and support urban and rural planning in the context of emerging climatic change.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Comparison of two split-window methods for retrieving land surface temperature from MODIS data

Land surface temperature (LST) is a key parameter in environment and earth science study, especially for monitoring drought. The objective of this work is a comparison of two split-window methods: Mao method and Sobrino method, for retrieving LST using MODIS (Moderate-resolution Imaging Spectroradiometer) data in North China Plain. The results show that the max, min and mean errors of Mao method are 1.33K, 1.54K and 0.13K lower than the standard LST product respectively; while those of Sobrino method are 0.73K, 1.46K and 1.50K higher than the standard respectively. Validation of the two methods using LST product based on weather stations shows a good agreement between the standard and Sobrino method, with RMSE of 1.17K, whereas RMSE of Mao method is 1.85K. Finally, the study introduces the Sobmao method, which is based on Sobrino method but simplifies the estimation of atmospheric water vapour content using Mao method. The Sobmao method has almost the same accuracy with Sobrino method. With high accuracy and simplification of water vapour content estimation, the Sobmao method is recommendable in LST inversion for good application in Ningxia region, the northwest China, with mean error of 0.33K and the RMSE value of 0.91K.     

西藏地表温度时空演变特征及影响因子

地表温度（land surface temperature, LST）是反映生态环境状况的重要指标。西藏作为气候变化的敏感地区,掌握其LST的时空变化有利于深入了解西藏热环境演化过程,为长期监测高原基础生态变化提供帮助。研究基于谷歌地球引擎获取西藏2000—2020年的MODIS LST数据,采用归一化分级方法对LST进行5个等级的划分,利用趋势分析、热力空间分析以及重心迁移等方法分析了研究区近20年来的LST时空演变特征。同时,选取归一化植被指数（normalized difference vegetation index, NDVI）、裸土指数（bare soil index, BI）、垂直不透水面指数（perpendicular impervious surface index, PISI）、湿度（WET）以及高程（digital elevation model, DEM）等5个影响LST的地表参数,结合多尺度地理加权回归,探讨了LST影响因子的作用尺度与作用效力。结果表明：2000—2020年,西藏LST均值由18.72 ℃上升至20.28 ℃,年均增长0.09 ℃,LST呈现微弱上升态势。20年来,LST在所有年份皆具有西北高、东南低的空间分布格局,LST增温趋势亦表现为西北高、东南低的分布特征。低温区和高温区空间分布聚集,形状简单、规则;次低温区、中温区以及次高温区空间分布破碎,形状复杂。2000—2020年各温区重心分布具有明显的方向性,且各温区重心迁移轨迹具有显著差异。特别是,低温区重心与高温区重心迁移轨迹呈现出由相向而行到背向而行的转变,反映出研究区东西部区域LST差距经历了由缩小到扩大的过程。DEM和WET对LST具有负向影响,BI、PISI和NDVI具有正向影响,常数项在不同生态区具有不同的影响性质。DEM具有较小的作用尺度以及最强的作用效力,常数项具有最小的作用尺度以及仅次于DEM的作用效力。     

Recent trends of temperature and precipitation proxies in Saudi Arabia: implications for climate change

The objective of the present study was to reconstruct a short-term (12–14 years) trend of surface temperature and precipitation patterns using their surrogates as provided by satellite images for selected locations along the Red Sea mountains in Saudi Arabia. Time series land surface temperature (LST) and normalized difference vegetation index (NDVI) data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were temporally plotted to delineate the trend and the decadal rates of change of both parameters. Results showed that real climate change is reported in the study area during the study period. There is a net increasing in the surface temperatures by 0.45 to 1.2 °C/decade and a net decrease in annual rainfall between 2001 and 2014. Findings of the present study show that the region is under a warming of the climate and a declining of wetness, which coincide with the air temperature and rainfall trends obtained from meteorological stations.     

Analysis of NVDI variability in response to precipitation and air temperature in different regions of Iraq,using MODIS vegetation indices

Iraq, the land of two rivers, has a history that extends back millennia and is the subject of much archaeological research. However, little environmental research has been carried out, and as such relatively little is known about the interaction between Iraq’s vegetation and climate. This research serves to fill this knowledge gap by investigating the relationship between the Normalized Difference Vegetation Index (NDVI) and two climatic factors (precipitation and air temperature) over the last decade. The precipitation and air temperature datasets are from the Water and Global Change Forcing Data ERA-Interim (WFDEI), and the NDVI dataset was extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m spatial resolution and 16 day temporal resolution. Three different climatic regions in Iraq, Sulaymaniyah, Wasit, and Basrah, were selected for the period of 2001–2015. This is the first study to compare these regions in Iraq, and one of only a few investigating vegetation’s relationship with multiple climatic factors, including precipitation and air temperature, particularly in a semi-arid region. The interannual, intra-annual and seasonal variability for each region is analysed to compare the different responses of vegetation growth to climatic factors. Correlations between NDVI and climatic factors are also included. Plotting annual cycles of NDVI and precipitation reveals a coherent onset, fluctuation (peak and decline), with a time lag of 4 months for Sulaymaniyah and Wasit (while for the Basrah region, high temperatures and a short rainy season was observed). The correlation coefficients between NDVI and precipitation are relatively high, especially in Sulaymaniyah, and the largest positive correlation was (0.8635) with a time lag of 4 months. The phenological transition points range between 3 and 4 month time lag; this corresponds to the duration of maturity of the vegetation. However, when correlated with air temperature, NDVI experiences an inverse relationship, although not as strong as that of NDVI and precipitation; the highest negative correlation was observed in Wasit with a time lag of 2 months (? 0.7562). The results showed that there is a similarity between temporal patterns of NDVI and precipitation. This similarity is stronger than that of NDVI and air temperature, so it can be concluded that NDVI is a sensitive indicator of the inter-annual variability of precipitation and that precipitation constitutes the primary factor in germination while the air temperature acts with a lesser effect.     

Satellite monitoring of spatio-temporal dynamics of China’s coastal zone eco-environments: preliminary analysis on the relationship between the environment,climate change and human behavior

In this paper, temporal dynamics of eco-environmental changes in coastal areas of China during 1981–2000 are investigated based on four key surface parameters including normalized difference vegetation index (NDVI), thermal index, moisture index and surface broadband albedo derived from quantitative remote sensing techniques and meteorological data. Firstly, land surface temperature (LST) and land surface broadband albedo are retrieved by the split-window algorithms and high-order polynomial regression method, respectively, using NOAA/AVHRR series images. Then, moisture index and thermal index, indicators of climate and moisture conditions in the study area, are computed from meteorological data and LST using principal component analysis (PCA). Finally, long-term dynamics of these eco-environmental factors and the reasons responsible for these changes are analyzed further. The results show that during the years from 1981 to 2000, the study area experienced a gradual increase in annual NDVI and climate factors and a decrease in surface annual broadband albedo, which indicates that the coastal thermal and moisture conditions and the subsistence conditions of natural vegetation have changed to a considerable extent. According to the results, a warming and wetting tendency over the last two decades is obvious in the China’s coastal zone that are mainly due to land use changes as of growing urbanization, exhaust emissions from industries and transportations and, partly global climate change. Uncontrolled rapid development of the study area may be blamed for these negative changes as a major driving force. The positive feedback mechanisms between albedo, NDVI and climate factors also partly explain these changes. This study suggests that the method integrating biophysical parameters retrieved from remote sensed images and meteorologic data provides a novel and feasible way to monitor large scale eco-environmental changes.

NDVI dynamic changes and their relationship with meteorological factors and soil moisture

Normalized difference vegetation index (NDVI) is an important indicator for measuring vegetation coverage, which is of great significance for evaluating vegetation dynamics and vegetation restoration. It can clearly analyze the suitable growth condition of vegetation by studying the relationship between meteorological factors, soil moisture and NDVI. Based on MODIS/NDVI data, the spatio-temporal characteristics of vegetation coverage in the Weihe River Basin (WRB) were analyzed by the trend analysis method. The relationship of NDVI with meteorological factors and NDVI with soil moisture simulated by the soil and water assessment tool (SWAT) model was analyzed in this paper. The results show that NDVI values gradually change with an increase from north to south in the WRB. The maximum of the average monthly NDVI is 0.702 (August) and the minimum is 0.288 in February from 2000 to 2015. The results of the seven grades of NDVI trend line slope indicate that the improvement area of vegetation coverage accounts for 30.93% of the total basin, and the degradation area and basically unchanged area account for 23% and 42.9%, respectively. The annual mean soil moisture is 19.37% in the WRB. There was a strong correlation between NDVI and precipitation, temperature, evaporation and soil moisture, and the correlation coefficients were 0.78, 0.89, 0.71 and 0.65, respectively. The ranges of the most suitable growth conditions for vegetation are 80–145 mm (precipitation), 13–23 °C (temperature), 94–144 mm (evaporation) and 25–33% (soil moisture), respectively.     

MODIS地表温度产品在青藏高原连续多年冻土区的适用性分析

利用自动气象站观测的长波辐射计算得到的地表温度对MODIS地表温度(LST)产品在青藏高原中部连续多年冻土区的精度进行验证, 并利用具有较高空间分辨率的Landsat 5 TM和Landsat 7 ETM+反演的地表温度与MODIS LST产品进行了对比分析. 结果表明: 白天MODIS LST产品的平均绝对误差(MAE)和均方根误差(RMSE)分别约为3.42~4.41 ℃和4.41~5.29 ℃, 夜晚MODIS产品MAE和RMSE分别为2.15~2.90 ℃和3.05~3.78 ℃, 精度高于白天; MODIS LST与TM、ETM+反演的地表温度一致性较好, 相关系数分别达到0.85和0.95. 说明MODIS LST产品在连续多年冻土区的适用性较高, 是研究多年冻土地表热状况的一个非常好的数据源. 而且, 不同空间尺度的遥感数据之间一致性较好, 可考虑将多源遥感数据应用于多年冻土热状况监测研究.     

The relationship between land surface temperature and land use/land cover in Guangzhou,China

The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI were effective indicators for quantifying LULC impacts on LST.     

基于植被指数和土地表面温度的干旱监测模型

干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品---土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。     

Soil CO2 efflux along an elevation gradient in Qinghai spruce forests in the upper reaches of the Heihe River,northwest China

This study was conducted in six plots along an elevation gradient in the Qinghai spruce (Picea crassifolia Kom.) forest ecosystem of the Qilian Mountains, northwest China. Soil CO₂ efflux over bare soil (R _s) and moss covered soil (R _s+m) were investigated from June to September in 2010 and 2011 by means of an automated soil CO₂ flux system (LI-8100). The results showed that R _s ranged from 1.51 to 3.96 (mean 2.64 ± 0.72) μmol m^?2 s^?1 for 2010, and from 1.41 to 4.09 (mean 2.55 ± 0.70) μmol m^?2 s^?1 for 2011. The daily change trend of R _s resembled that of air temperature (T _a), and there was a hysteresis between R _s and soil temperature (T _s). The seasonal variations of R _s at lowlands (i.e., Plot 1, Plot 2 and Plot 3) were driven by soil moisture and temperature (T _a and T _s), while that at highlands (i.e., Plot 4, Plot 5 and Plot 6) were obviously affected by temperature. There were higher values at Plot 2 and Plot 6, which were caused by the interaction between soil moisture and temperature. In addition, soil CO₂ efflux over moss covered soil (R _s+m) was 8.83 % less than that over bare soil (R _s), indicating that moss was another factor affecting R _s. It was concluded that R _s had temporal and spatial variations and was mainly controlled by temperature and soil moisture; the main determinants differed at different elevations; moss could reduce R _s.     

S patio-temporal pattern of land use and land cover and its effects on land surface temperature using remote sensing and GIS techniques: a case study of Bhubaneswar city,Eastern India (1991–2021)

Urbanization produces substantial land use changes by causing the construction of different urban infrastructures in the city region for habitation, transportation, industry, and other reasons. As a result, it has a significant impact on Land Surface Temperature (LST) by disrupting the surface energy balance. The objective of this paper is to assess the impact of land-use/land-cover (LU/LC) dynamics on urban land surface temperature (LST) of Bhubaneswar City in Eastern India during 30 years (1991–2021) using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms (MLA). The finding reveals that the mean LST over the entire study domain grows significantly between 1991 and, 2021due to urbanization (β coefficient 0.400, 0.195, 0.07, and 0.06 in 1991, 2001, 2011, and 2021 respectively) and loss of green space (β coefficient − 0.295, − 0.025, − 0.125 and − 0.065 in 1991, 2001, 2011 and 2021 respectively). The highest class recorded for agricultural land (49.60 km², accounting for 33.94% of the total land area) was in 1991 followed by vegetation (41.27 km², 28.19% of the total land area), and built-up land (27.59 km², 18.84% of the total land area). The sharp decline of vegetation cover will continue until 2021 due to increasing built-up areas (r = − 0.531, − 0.329, − 0.538, and − 0.063 in the 1991, 2001, 2011 and 2021 respectively). Built-up land (62.60 km², accounting for 42.76% of the total land area, an increase of 35.01 km² from 1991) as the highest class followed by water bodies (21.57%, 32.60 km² of the land area), and agricultural land (31.57 km², 21.57% of the land area) in 2021. Remote sensing techniques proved to be an important tool to urban planners and policymakers to take adequate steps to promote sustainable development and minimize urbanization influence on LST. Urban green space (UGS) can help improve the overall liveability and environmental sustainability of Bhubaneswar city.

     

Importance of Biogeomorphic and Spatial Properties in Assessing a Tidal Salt Marsh Vulnerability to Sea-level Rise

We evaluated the biogeomorphic processes of a large (309 ha) tidal salt marsh and examined factors that influence its ability to keep pace with relative sea-level rise (SLR). Detailed elevation data from 1995 and 2008 were compared with digital elevation models (DEMs) to assess marsh surface elevation change during this time. Overall, 37 % (113 ha) of the marsh increased in elevation at a rate that exceeded SLR, whereas 63 % (196 ha) of the area did not keep pace with SLR. Of the total area, 55 % (169 ha) subsided during the study period, but subsidence varied spatially across the marsh surface. To determine which biogeomorphic and spatial factors contributed to measured elevation change, we collected soil cores and determined percent and origin of organic matter (OM), particle size, bulk density (BD), and distance to nearest bay edge, levee, and channel. We then used Akaike Information Criterion (AICc) model selection to assess those variables most important to determine measured elevation change. Soil stable isotope compositions were evaluated to assess the source of the OM. The samples had limited percent OM by weight (<5.5 %), with mean bulk densities of 0.58 g cm^-3, indicating that the soils had high mineral content with a relatively low proportion of pore space. The most parsimonious model with the highest AICc weight (0.53) included distance from bay's edge (i.e., lower intertidal) and distance from levee (i.e., upper intertidal). Close proximity to sediment source was the greatest factor in determining whether an area increased in elevation, whereas areas near landward levees experienced subsidence. Our study indicated that the ability of a marsh to keep pace with SLR varied across the surface, and assessing changes in elevation over time provides an alternative method to long-term accretion monitoring. SLR models that do not consider spatial variability of biogeomorphic and accretion processes may not correctly forecast marsh drowning rates, which may be especially true in modified and urbanized estuaries. In light of SLR, improving our understanding of elevation change in these dynamic marsh systems will play a crucial role in forecasting potential impacts to their sustainability and the survival of these ecosystems.     

Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Particulate matter concentration and assessment of its movement pattern is crucial in air pollution studies. However, no study has been conducted to determine the PM₁₀ concentration using atmospheric correction of thermal band by temperature of nearest dark pixels group (TNDPG) of this band. For that purpose, 16 Landsat Enhanced Thematic Mapper plus ETM+ images for Sanandaj and Tehran in Iran were utilized to determine the amount of PM₁₀ concentration in the air. Thermal infrared (band 6) of all images was also used to determine the ground station temperature (GST b6) and temperature of nearest dark pixels group. Based on atmospheric correction of images using temperature retrieval from Landsat ETM+, three empirical models were established. Non-linear correlation coefficient with polynomial equation was used to analyze the correlations between particulate matter concentration and the ground station temperature for the three models. Similar analyses were also undertaken for three stations in Klang Valley, Malaysia, using 11 Landsat ETM+ images to show the effectiveness of the model in different region. The data analysis indicated a good correlation coefficient R = 0.89 and R = 0.91 between the trend of the result of temperature of nearest dark pixels group b6 ? (GST b6 ? GST) model and the trend of PM₁₀ concentration in Iran and Malaysia, respectively. This study reveals the applicability of the thermal band of Landsat TM and ETM+ to determine the PM₁₀ concentration over large areas.