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1.
The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R2?=?0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R2?=?0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R2?=?0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.  相似文献   

2.
Regional estimates of soil carbon pool have been made using various approaches that combine soil maps with sample databases. The point soil organic carbon (SOC) densities are spatialized employing approaches like regression, spatial interpolation, polygon based summation, etc. The present work investigates a data mining based spatial imputation for spatial assessment of soil organic carbon density. The study area covers Andhra Pradesh and Karnataka states of India. Field sampling was done using stratified random sampling method with land cover/use, soil type, agro-ecological regions for defining strata. The spatial data at 1 km resolution on climate, NDVI, land cover, soil type, topography was used as input for modeling the top 30 cm Soil Organic Carbon (SOC) density. To model the SOC density, a Random Forest (RF) based model with optimal parameters and input variables has been adopted. Experiment results indicate that 500 number of trees with 5 variables at each split could explain the maximum variability of soil organic carbon density of the study area. Out of various input variables used to model SOC density, land use / cover was found to be the most significant factor that influences SOC density with a distinct importance score of 34.7 followed by NDVI with a score of 12.9. The predicted mean SOC densities range between 2.22 and 13.2 Kg m?2 and the estimated pool size of SOC in top 30 cm depth is 923 Tg for Andhra Pradesh and 1,029 Tg for Karnataka. The predicted SOC densities using this model were in good agreement with the measured observations (R?=?0.86).  相似文献   

3.
 首先,利用Landsat TM热红外影像结合地面气象观测资料反演地面温度,揭示了济南市夏季城市热岛效应| 然后,基于稳 健的LTS与最小二乘回归（LS）分析探讨了城乡地面热辐射与地表特征参数的线性变化趋势,认为植被指数（NDVI、SAVI和TCG）、 湿度指数（NDMI和TCW）以及近红外反照率与地表温度的变化趋势相反,亮度指数（NDBI和TCB）和可见光反照率与地表温度的变化 趋势一致,而短光波段反照率与地表温度不存在明显相关趋势。研究结果表明,NDMI能很好地解释地表温度变化,且最为稳健; 其次是NDVI、SAVI、TCG和NDBI,它们对地表温度的解释程度高且稳健性较强; 可见光反照率虽能较好解释地表温度,但其稳健性 较差; 近红外反照率、TCW和TCB对地表温度的解释程度和稳健性相对较低。  相似文献   

4.
Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.  相似文献   

5.
The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.  相似文献   

6.
基于TM图像的“增强的指数型建筑用地指数”研究   总被引：6，自引：0，他引：6  
以Landsat TM/ETM+图像为数据源,研究城镇和农村建筑用地信息的提取方法.首先利用TM7,4,2波段创建归一化差值裸地与建筑用地指数(normalized difference bareness and built- up index,NDBBI);然后根据裸地在裸土指数(bare doil index,BSI)图像上的亮度值最高、在改进型归一化差值水体指数(modified normalized difference water index,MNDWI)图像的亮度值最低的特征,提出了增强型裸土指数(enhanced baresoilindex,EBSI);最后选用NDBBI,EBSI,MNDWI和SAVI( soil adjustment vegetation index,SAVI)4个指数,构建一种新型的建筑用地指数,称为“增强的指数型建筑用地指数”( enhanced index - based built - up index,EIBI),可快速地提取建筑用地信息.实验结果表明,用EIBI提取的建筑用地信息客观,人为干预少,可信度高,提取精度可达90％以上,适合于同时提取城市和农村建筑用地信息.  相似文献   

7.
The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.  相似文献   

8.
We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r2 = 0.56–0.77 AIC = 0.16–0.30 for SR models; r2 = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVIMODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVIMSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVIMSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVIMODIS data (r2 = 0.68 for SC for non-grazed systems, modeled with SR based on NDVIMODIS data; r2 = 0.77 for SC for non-grazed systems, modeled with SR based on NDVIMSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.  相似文献   

9.
Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index have promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis + NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI1) (CVA = 79.55%) and Vis + NIR index (NDVI, NG, IPVI and GNDVI) (CVA = 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.  相似文献   

10.
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.  相似文献   

11.
Forests play a critical role in ecological functioning, global warming and climate change through its unique potential to capture and hold carbon (C). Biomass is one of the indicator of the status of forests hence accurate assessment and biomass mapping is important for sustainable forest management. The objectives of this study is to estimate above ground biomass (AGB) from field inventory data and to map AGB combining field inventory data, remote sensing and geo-statistical model. In the present study stratified random sampling were used for estimation of biomass in which 59 plots were laid down in different homogenous strata depending on the NDVI values for the region of Maharashtra Western Ghats. The above ground biomass from field ranged from 0.05 to 271 t-dry wt ha?1 in which trees added maximum towards total biomass followed by shrubs and herbs. This paper evaluates the best vegetation indices to estimate biomass. This study was carried out by using Landsat TM satellite data and field inventory data in the Ratnagiri district of Maharashtra, India. A significant correlation was observed between biomass and vegetation indices. The best fit regression equation developed from field above ground biomass and NDVI with R2 value of 0.61 was used for spectral modeling to estimate the geospatial distribution of AGB in the entire region. The results of spatial predictions Geostatistical technique and remotely sensed data as auxiliary variables were compared using statistical error methods. This study employed Mean error, Root-Mean-Square error, Average Standard error and Root-Mean Square Standardized error. The ME, RMSE, Average Standard error and Root-Mean Square Standardized error was 0.078, 8.032, 7.982 and 0.967 respectively. The results showed that cokriging technique is one of the geostatistical method for spatial predictions of biomass in the studied region. The present study revealed that remote sensing technique combined with field sampling provides quick and reliable estimates of above ground biomass and carbon pool and can be used as baseline information for further temporal studies of biomass status of the region and in planning of forest and natural resources management.  相似文献   

12.
Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.  相似文献   

13.
Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated.  相似文献   

14.
ABSTRACT

Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir; hence soils under commercial forests could store a significant amount of carbon. However, the variability of soil organic carbon (SOC) within forest landscapes is still poorly understood. Due to limitations encountered in traditional systems of SOC determination, especially at large spatial extents, remote sensing approaches have recently emerged as a suitable option in mapping soil characteristics. Therefore, this study aimed at predicting soil organic carbon (SOC) stocks in commercial forests using Landsat 8 data. Eighty-one soil samples were processed for SOC concentration and fifteen Landsat 8 derived variables, including vegetation indices and bands were used as predictors to SOC variability. The random forest (RF) was adopted for variable selection and regression method for SOC prediction. Variable selection was done using RF backward elimination to derive three best subset predictors and improve prediction accuracy. These variables were then used to build the RF final model for SOC prediction. The RF model yielded good accuracies with root mean square error of prediction (RMSE) of 0.704 t/ha (16.50% of measured mean SOC) and 10-fold cross-validation of 0.729 t/ha (17.09% of measured mean SOC). The results demonstrate the effectiveness of Landsat 8 bands and derived vegetation indices and RF algorithm in predicting SOC stocks in commercial forests. This study provides an effective framework for local, national or global carbon accounting as well as helps forest managers constantly evaluate the status of SOC in commercial forest compartments.  相似文献   

15.
Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ ?1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > ?1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.  相似文献   

16.
In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.  相似文献   

17.
This study demonstrates the potentials of IRS P6 LISS-IV high-resolution multispectral sensor (IGFOV  6 m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the relationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and field measured leaf area index (ELAI) and estimated above-ground biomass (EAGB) were derived. Remote sensing (RS) data-based leaf area index (PLAI) image is generated using regression equation based on NDVI and ELAI (r2 = 0.68, p ≤ 0.05). RS-based above-ground biomass (PAGB) image was generated based on regression equation developed between PLAI and EAGB (r2 = 0.63, p ≤ 0.05). The mean value of estimated above-ground biomass and RS-based above-ground biomass in the study area are 280(±72.5) and 297.6(±55.2) Mg ha−1, respectively. The regression models generated in the study between NDVI and LAI; LAI and biomass can also help in generating spatial biomass map using RS data alone. LISS-IV-based estimation of biophysical parameters can also be used for the validation of various coarse resolution satellite products derived from the ground-based measurements alone.  相似文献   

18.
Spatial Variability and Precision Nutrient Management in Sugarcane   总被引：1，自引：0，他引：1  
Investigations were carried out to develop precision nutrient management techniques for sugarcane. The study area (800 ha) comprised of Bijapur, Bilgi and Jamakhandi talukas that lie between 16° 34′–28° 10′ N latitudes and 75° 33′–75° 37′ E longitudes and located around Nandi Sahakari Sakkare Karkhane (NSSK) Niyamit, Galagali. The soils are medium to deep black with pH and EC ranging from 7.32 to 8.36 and 0.17 to 1.13 dS/m, respectively. The soils are low to medium in available nitrogen, medium in available phosphorus and high in available potassium content. Crop condition assessment was made through analysis of LISS-III satellite images using Erdas Imagine software. Fertigation with 300 kg N and 195 kg K per ha at fortnightly interval and soil application of 32 kg P per ha as basal, recorded higher sugarcane yield (167 Mg ha?1) as compared to 124 Mg ha?1 obtained with soil application of 250 kg N, 32 kg P and 156 kg K per ha and flood irrigation as per the package recommended by the University(POP). Fertigation of N and K at weekly interval recorded highest NDVI value (0.354) and soil application of nutrients as per POP resulted in the lowest NDVI of 0.219.  相似文献   

19.
The best and commonly used ground-based sensor to monitor crop growth, ASD FieldSpecPro Spectroradiometer (Analytical Spectral Devices, Boulder, CO, USA) is a passive sensor, which can be used under adequate light condition. However, now-a-days active sensors such as GreenSeeker? (GS) handheld crop response (Trimble Agriculture division, USA) are used for monitoring crop growth and are flexible in terms of timeliness and illumination conditions besides being cheaper than the ASD. Before its wide use, the suitability and accuracy of GS should be assessed by comparing the NDVI measured by this instrument with that by ASD, under diverse wheat growing conditions of India. Keeping this in view, the present experiment was undertaken with the following objectives: (1) to find out the temporal variation of NDVI measured both by ASD and GS treatments, (2) to find out relationship between the NDVI measured through ASD and GS and, (3) to evaluate the suitability of GS for NDVI measurements. It was observed that the numerical value of NDVI as measured by GS was always significantly (P < 0.05) lower than that measured by ASD for all the experiments under study. The NDVI-ASD and NDVI-GS were significantly positively correlated (P < 0.01) with the correlation coefficients being +0.94, +0.88 and +0.87 for irrigation and nitrogen experiment, irrigation and cultivars experiment, and tillage, residue and nitrogen experiments, respectively. Further, the regression equation developed between the NDVI-ASD and NDVI-GS: [NDVI-GS = 1.070 × (NDVI-ASD ? 0.292] can be successfully used to compute the NDVI of ASD from that computed by GS.  相似文献   

20.
Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO2, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO2 plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO2 on LST, the relationship between LST, air temperature and CO2 was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R2 = 0.994), May (R2 = 0.297) and June (R2 = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO2 levels were obtained from linear regression analysis. band 11 was correlating significantly with CO2 values in each of the months April (R2 = 0.217), May (R2 = 0.914) and June, (R2 = 0.934), because band 11 is closer to the 15-micron band of CO2. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.  相似文献   

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