Landsat TM Bands Research Articles

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0–20 cm) in a 2574 km2 agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (−0.26 > r > −0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia, Argentina

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions. Globally, arid and semi-arid regions are considered an important carbon sink. However, they have received limited attention and, therefore, it should be a priority to develop tools to quantify biomass at the local and regional scales. Individual plant variables, such as stem diameter and crown area, were reported to be good predictors of individual plant weight. Stand-level variables, such as plant cover and mean height, are also easy-to-measure estimators of above-ground biomass (AGB) in dry regions. In this study, we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia, Argentina. We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables. We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass. For this purpose, we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables. We obtained a model-based ratio estimate of regional mean AGB and its standard error. Total plant cover allowed us to obtain a reliable estimation of local AGB, and no better fit was attained by the inclusion of other structure variables. The stand-level plant cover ranged between 18.7% and 95.2% and AGB between about 2.0 and 70.8 Mg/hm2. AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3, which facilitated a model-based ratio estimate of the regional mean AGB (approximately 12.0 Mg/hm2) and its sampling error (about 30.0%). The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands. Thus, plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass, as well as to assess and monitor the impact of land-use change on the carbon balance, for arid and semi-arid regions.

A soil colour map of China

Soil colour can indicate soil physical, chemical and biological properties and processes, and is an important indicator for soil classification, soil quality evaluation and soil management. It varies in both horizontal and vertical dimensions, and thus regional maps of soil colour can reveal spatial patterns of these properties, processes, and indicators. However, although soil regions are sometimes named for their dominant soil colour, it is directly measured only at “point” support, i.e., during soil profile description, whereas it is desirable to know soil colour over the entire soilscape. To achieve this for China we used predictive soil mapping methods to produce soil colour maps (dry and moist colours) at 1 km2grid cell size and over multiple depths from a consistent dataset of approximately 4 600 full profile descriptions taken as part of a national survey to define soil series in Chinese Soil Taxonomy, and a set of environmental covariates covering the national territory. The covariates characterized soil forming factors including climate, parent materials, terrain, vegetation, land surface water and thermal conditions. Soil colour descriptions in the Munsell system were extracted from the genetic horizon descriptions at the selected depths and converted to the sRGB and L*a*b* colour spaces. Dry and moist colour separates were not well-correlated in either space (r<0.76). Random forest models were constructed in both spaces, for dry and moist colours separately. Models in sRGB space were moderately successful (R2≈0.43,RMSE≈26/255) at 5 cm, with success decreasing with depth. Models smoothed the colour space and thus did not predict the more extreme values or chromas, nor the rarer hues. Models in L*a*b* space were less successful. The fitted sRGB models were used to produce predictive maps over all of China. Regional patterns as well as local detail are clearly shown. Solar radiation, wind exposure, regolith thickness, and Landsat TM bands 7 and 5 contributed most to the predictions, followed by elevation, mean annual precipitation, terrain wetness index, air temperature seasonality, precipitation standard deviation and standard deviation of NDVI. These suggest pedological processes acting on the development of soil colours, including weathering of parent materials, oxidation-reduction chemistry and biochemistry of the decomposing of organic matter. This study shows that predictive methods from points using suitable covariates are an alternative to spatial predictions over map units from their representative profiles.

Image texture indices and trend analysis for forest disturbance assessment under wood harvest regimes

Effective disturbance indices for Hyrcanian forests in Kheyroud, Nowshahr, Iran were determined. The study area was divided into landscape mosaics based on ecosystem parameters including profile type, slope and elevation. Co-occurrence texture indices were derived as forest disturbance factors on the first five bands of Landsat TM, ETM+ and OLI images for the prevailing wood harvest disturbance regimes. These indices were screened using ten types of trend analyses and used for modeling disturbance of the harvesting regime through artificial neural networks. The results show that the selected indices can be useful in distinguishing areas with different disturbance intensities and as such, used in the context of health assessment through the health distance method. The accuracy of the health maps derived from the indices [increasing disturbance] led to give rise higher disturbance classification accuracy.

Detection of mineral alteration induced by hydrocarbon microseepages by using remotely sensed data in the Fateh Jang area of the Northern Potwar region of Pakistan

Mineral alteration can be induced by the hydrocarbon seepages at the earth surface that can be detected by the use of earth observation data. The long-term seepage of hydrocarbons in the subsurface can induce different types of chemical and mineralogical alterations in the rosk and soil. Mapping of these mineral alterion is important to explore hydrocarbon. In this study, satellite images were used to identify the surface mineral alterations due to hydrocarbon microseepages in the Fateh Jang area of the Northern Potwar region, Islamabad, Pakistan. Landsat TM band ratios of 7/5, 3/1, and 5/4 are used to detect ferric minerals, clay minerals, and ferrous iron minerals bearing sedimentary rocks, respectively, which are applied to distinguish the altered and unaltered rocks. Such rocks have specific spectral responses in various bands of the electromagnetic spectrum and thus give rise to a number of anomalies indicating the presence of hydrocarbons. This study uses enhancement techniques of principal component analysis (PCA), band ratio, false color composite (FCC), and thermal anomaly composition for surface expressions caused by microseeps. It is revealed that spectral reflectance signify that the ferrous iron and red bed bleaching have reflection band and absorption band in TM bands 1, 3, and 4, respectively. Clay minerals and kaolinite have enormous reflection in band 5 and absorption capacity in TM band 7. These results can be used as a model to identify the alteration induced by hydrocarbon seepages. The study area is categorized into northern, eastern, southern, and western zones on the basis mineral alteration. The northern zone is characterized by strong anomalies and is relatively rich in clay alteration and ferrous as compared to other zones. Two types of hydrocarbon microseepages were observed in the Fateh Jang area which includes active seepages (gas microseepages and oil seepage) and passive seepage (bleaching effects).

Intra-annual phenology for detecting understory plant invasion in urban forests

Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense.

Spectral evidence of early-stage spruce beetle infestation in Engelmann spruce

Spruce beetle (Dendroctonus rufipennis (Kirby)) outbreaks cause widespread mortality of Engelmann spruce (Picea engelmannii (Parry ex Engelm)) within the subalpine forests of the western United States. Early detection of infestations could allow forest managers to mitigate outbreaks or anticipate a response to tree mortality and the potential effects on ecosystem services of interest. However, the subtle changes in the foliage of infested spruce make early detection difficult. An experiment was conducted in southern Colorado to determine important wavelengths for detecting early-stage (i.e. recently infested) spruce beetle infestation in Engelmann spruce. Spectral reflectance from non-infested and recently infested spruce needles were obtained using the ASDi Field-Spec Pro spectroradiometer. After pre-processing, random forest analysis was used to identify hyperspectral bands and aggregations of hyperspectral bands corresponding to Landsat TM bands and vegetation indices that effectively discriminated between non-infested and infested trees. Results show that the shortwave infrared region of the electromagnetic spectrum was a key area for detecting early stages of spruce beetle infestation, likely due to the effects of beetle infestation on water transport within Engelmann spruce. The strong discriminability of bands in the shortwave infrared region indicates a potential for this spectral region to be used to detect early-stage spruce beetle infestation over larger areas using multispectral satellite imagery. In a preliminary trial, we found that a time series of reflectance in Landsat TM band 7 (shortwave infrared) was strongly correlated with the progression through time of a spruce beetle outbreak in southern Wyoming. These findings suggest that multispectral indicators of early-stage spruce beetle outbreak can be developed. These indicators are needed to better understand spatiotemporal dynamics of spruce beetle outbreaks, and can be used by forest managers to detect early stages of spruce beetle infestation and to potentially mitigate some spruce mortality.

Relationships between soil spectral and chemical properties along a climatic gradient in the Judean desert

ABSTRACTRegions of rainfall gradients at desert margins are areas where eco-geomorphic changes and degradation in response to global warming and grazing are most intensive. Assessment and understanding of the consequences of global warming and human disturbance of these vulnerable ecosystems requires detailed information regarding the spatial variation of soil and vegetation properties and their dependence on rainfall. Such detailed information is still of limited availability. This study aimed to rectify this shortcoming by analyzing data on the chemical and spectral properties of soil samples collected along the Judean Desert climatic gradient. Six clusters of typical chemical soil properties were identified. Dependencies of clusters’ chemical properties (total iron, organic and inorganic carbon) on annual rainfall amounts were found to be significant. Two spectral Landsat TM band ratios introduced to assess soil conditions showed significant correlations with these chemical properties. These ratios were found to be higher correlated than individual chemical properties with average rainfall suggesting that spectral data may represent a broader range of chemical, physical, and biological site properties. Examining the three-way relationships between soil chemical properties, their spectral reflectance characteristics and rainfall, facilitates a better understanding of the generalized trends along the climatic gradient and more specifically of modes of transition from semi-arid to arid zones.

A Simple Enhanced Water Index (EWI) for Percent Surface Water Estimation Using Landsat Data

To timely obtain accurate pixel water surface proportion information through remote sensing is extremely significant to the ecological restoration in inland river basins and for the precise management of water resources. In respect to the insufficient extraction of water surface proportion information present in pixels in most of the current water information models, a simple model Enhanced Water Index (EWI) based on Modified Normalized Difference Water Index (MNDWI) has been introduced. EWI, which is oriented toward the sub-pixel level analysis of water surface proportion mapping of inland river basin, has been put forward based on the analysis of typical spectral signatures such as desert, soil, and vegetation along with MNDWI in accordance with the Landsat TM band features. The analysis is done by using methods of pixel-based EWI value with different water proportions which are analyzed through the introduction of the linear hybrid simulation between the water body and the corresponding background. Lastly, the effect of EWI model has been tested in the medium and lower reaches of Tarim. The correction coefficient for sub-pixel level water surface proportion predicted by the EWI model and the experimental data is $\bf{R}^{\bf{2}} = \bf{0.72}$ . Results showed that the model was able to effectively extract the information about pixel water surface proportion in inland river basins. This study proves that EWI model has great potential in its application for water proportion mapping applications.

Comparison of two models for decomposition of land surface temperature image using Landsat TM data

Land surface temperature( LST) is a vital parameter controlling the energy and water balance between atmosphere and land surface. LST image with high spatial resolution compatible with visible bands of Landsat TM is very important for the application of the LST image to many studies such as environmental monitoring. This paper examines the accuracy and applicability of two widely- used models for decomposition of LST images: SUTM and E- Distrad. Landsat TM data acquired in Beijing were used for the study. LST retrieved by the mono- window algorithm( MWA) was used to compare the LST decomposition images by the two models. The results achieved by the authors indicate that SUTM is more applicable than E- Distrad in the regions with low vegetation cover and high LST such as downtown,while the latter is better than the former in the high vegetation cover and relatively cold areas such as water bodies. The RMSE and MAE are 1. 522 K and 1. 191 K respectively for SUTM and 1. 768 K and 1. 173 K for E- Distrad. It is thus concluded that both models are applicable for decomposition of LST images for high spatial resolution,but the results of decomposition are different in areas of different vegetation covers.

Assessment of underground water potential zones using modern geomatics technologies in Jhansi district, Uttar Pradesh, India.

Abstract. Ground water is a distinguished component of the hydrologic cycle. Surface water storage and ground water withdrawal are traditional engineering approaches which will continue to be followed in the future. The uncertainty about the occurrence, distribution and quality aspect of the ground water and the energy requirement for its withdrawal impose restriction on exploitation of ground water. The main objective of the study is assessment of underground water potential zones of Jhansi city and surrounding area, by preparing underground water potential zone map using Geographical Information System (GIS), remote sensing, and validation by underground water inventory mapping using GPS field survey done along the parts of National Highway 25 and 26 and some state highway passing through the study area. Study area covers an area of 1401 km2 and its perimeter is approximate 425 km. For this study Landsat TM (0.76–0.90 um) band data were acquired from GLCF website. Sensor spatial resolution is 30 m. Satellite image has become a standard tool aiding in the study of underground water. Extraction of different thematic layers like Land Use Land Cover (LULC), settlement, etc. can be done through unsupervised classification. The modern geometics technologies viz. remote sensing and GIS are used to produce the map that classifies the groundwater potential zone to a number of qualitative zone such as very high, high, moderate, low or very low. Thematic maps are prepared by visual interpretation of Survey of India topo-sheets and linearly enhanced Landsat TM satellite image on 1 : 50,000 scale using AutoCAD, ArcGIS 10.1 and ERDAS 11 software packages.

Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery

Maps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image textural analysis on Landsat TM imagery. We investigate eight gray level co-occurrence matrix based texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) on four window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9) at four offsets ([1,0], [1,1], [0,1], [1,−1]) on four Landsat TM bands (2, 3, 4, and 5). The map with the highest prediction quality was created using three texture metrics calculated from Landsat Band 2 on a 3 × 3 window and an offset of [0,1]: entropy, mean and correlation; and one physical variable: slope. The correlation of predicted versus observed biomass values for our texture-based biomass map is r = 0.86, the Root Mean Square Error is 45.6 Mg∙ha−1, and the Coefficient of Variation of the Root Mean Square Error is 0.31. We find that models including image texture variables are more strongly correlated with biomass than models using only physical and spectral variables. Additionally, we suggest that the use of texture appears to better capture the magnitude and direction of biomass change following disturbance compared to spectral approaches. The biomass mapping methods we present here are widely applicable throughout the US, as they are based on publically available datasets and utilize relatively simple analytical routines.

Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, south Iran

Spectral transformation methods, including correlation coefficient (CC) and Optimum Index Factor (OIF), band ratio (BR) and principal component analysis (PCA) were applied to ASTER and Landsat TM bands for lithological mapping of Soghan ophiolitic complex in south of Iran. The results indicated that the methods used evidently showed superior outputs for detecting lithological units in ophiolitic complexes. CC and OIF methods were used to establish enhanced Red–Green–Blue (RGB) color combination bands for discriminating lithological units. A specialized band ratio (4/1, 4/5, 4/7 in RGB) was developed using ASTER bands to differentiate lithological units in ophiolitic complexes. The band ratio effectively detected serpentinite dunite as host rock of chromite ore deposits from surrounding lithological units in the study area. Principal component images derived from first three bands of ASTER and Landsat TM produced well results for lithological mapping applications. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinite dunite in ophiolitic complexes. The developed approach used in this study offers great potential for lithological mapping using ASTER and Landsat TM bands, which contributes in economic geology for prospecting chromite ore deposits associated with ophiolitic complexes.

Mapping amyotrophic lateral sclerosis lake risk factors across northern New England

BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.MethodsOur eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots.ResultsSeveral distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R2 values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively.ConclusionsThe strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3×3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77×10(6) Mg in 1999 to 2.73×10(6) Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9×10(3) ha in 1999 to 18.1×10(3) ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.

Hydro-physical processes at the plunge point: an analysis using satellite and in situ data

Abstract. The plunge point is the main mixing point between river and epilimnetic reservoir water. Plunge point monitoring is essential for understanding the behavior of density currents and their implications for reservoir. The use of satellite imagery products from different sensors (Landsat TM band 6 thermal signatures and visible channels) for the characterization of the river-reservoir transition zone is presented in this study. It is demonstrated the feasibility of using Landsat TM band imagery to discern the subsurface river plumes and the plunge point. The spatial variability of the plunge point evident in the hydrologic data illustrates the advantages of synoptic satellite measurements over in situ point measurements alone to detect the river-reservoir transition zone. During the dry season, when the river-reservoir water temperature differences vanish and the river circulation is characterized by interflow-overflow, the river water inserts into the upper layers of the reservoir, affecting water quality. The results indicate a good agreement between hydrologic and satellite data and that the joint use of thermal and visible channel data for the operational monitoring of a plunge point is feasible. The deduced information about the density current from this study could potentially be assimilated into numerical models and hence be of significant interest for environmental and climatological research.

Modeling of multi-strata forest fire severity using Landsat TM Data

Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.

Analysis of Thermal Characteristics for Areas of Musim Stream in Cheongju City

The urban thermal environment can be an important index to detect heat island phenomena and manage it to improve urban life quality. Cheongju is a typical plain-city that main part has been formed and developed in lowland. The Mushim stream crosses the city from south to north. We reviewed the use of thermal remote sensing in stream around areas and the thermal environments, focusing primarily on the Urban Heat Island（UHI) effect. The purpose of this study is to determine the relationship between the stream nearby urban area and the stream cooling effect of UHI. The objectives are to determine the usefulness of KOMPSAT-2 bands MS3 and MS4 for vegetation cover mapping, and the usefulness of LANDSAT TM band 6 in identifying thermal environmental characteristics and UHI. Land Surface Temperatures (LST) are retrieved by single-channel algorithm to study the UHI from the 6th band (thermal infrared band) of LANDSAT TM images and thermal radiance thermometer based on remote sensing method and the LST distribution maps are accomplished according to the retrieval results. There is also comparison of satellite-derived and in situ measured temperature. The results indicated that the LST of urban center is higher than that of suburban area, the temperature of mountain and water are the lowest area, so it is clearly proved that there are obvious UHI effects by stream. The surface temperature distribution of Mushim stream is detected 2℃ lower than urban area.

Mapping Topsoil Organic Carbon in Non-agricultural Semi-arid and Arid Ecosystems of Israel

Mapping of soil organic carbon (SOC) was accomplished with remote sensing methods to assess its spatial variability. The relationship between bi-directional reflectance measurements and SOC was investigated with respect to C.I.E. color coordinates. Empirical relationships were generated for the spectral detection of SOC of two semi-arid to arid study sites. These regression models allowed the prediction of SOC with a cross-validated r z = 0.910 (RMSE cv = 2.825) and r 2 of 0.795 (RMSE cv = 2.113), respectively. Because C.I.E. color coordinates were found to be appropriate parameters for predicting the SOC, reflectance values of Landsat TM bands were transformed into C.I.E. color coordinates. The C.I.E. based regression models were applied to a Landsat image to estimate SOC in the spatial domain. Concentrations predicted from satellite data corresponded well with concentration ranges derived from chemical analysis. Estimated concentrations reflect the geographic conditions and depend on annual rainfall, with a general trend to decreasing SOC with increasing aridity.