Landsat Thematic Mapper Data Research Articles

Estimating upper soil horizon carbon stocks in a permafrost watershed of Northeast Siberia by integrating field measurements with Landsat-5 TM and WorldView-2 satellite data

Arctic soils contain three times as much carbon (C) as all aboveground biomass distributed globally, much of which is stored in permafrost soils. Here, we (1) determine the predictability of estimating soil organic carbon (SOC) using different satellite data, classifications, and methods; (2) estimate the quantity and distribution of SOC for the top 10 cm for the Ambolikha River watershed (~121 km2) in northeast Siberia, a sub-watershed of the Kolyma River; and (3) produce a hybrid SOC map through data fusion, combining the strengths of each data type. Land cover maps were produced using a pixel-based classification with Landsat-5 Thematic Mapper (TM) data and an object-based classification using WorldView-2 data. Spectral mixture analysis (SMA) was performed on both data types to calculate the fraction of four vegetation types in each pixel, and land cover maps were combined with field measurements of SOC in the top 10 cm. The overall classification accuracy was 69% for Landsat, 81% for WorldView-2, and 82% for the hybrid map. The hybrid map estimated 490.5 Gg of SOC for the top 10 cm within the Ambolikha River watershed, with less severe over- or under-estimated than the Landsat or WorldView-2 maps alone. The results suggest that (a) higher spatial resolution satellite data and object-based classification should be used for land cover classification in the Arctic; (b) integrating multiple sensors maximizes the strengths of different sensors for estimating SOC; and (c) previous studies performed at the regional or pan-Arctic scale using lower spatial resolution data likely underestimate total SOC.

Spaceborne detection of roof and impervious surface albedo: Potentialities and comparison with airborne thermography measurements

The albedo of urban roofs and impervious surfaces constitutes one of the key parameters in the energy balance of the city environment, and its detailed and accurate assessment represents an ambitious and challenging objective.In this work, an analysis of Landsat Thematic Mapper (TM) data and airborne infrared (IR) thermography data over the city of Florence was performed to assess the satellite sensor capability to successfully detect the surface albedo with a high level of detail over a heterogeneous urban texture. The effect of the TM reflective bands spatial resolution (30m) on the albedo retrieval of single roofs, squares or roads was investigated, comparing these data with the aircraft surface temperature measurements at 1m spatial resolution, which are indirectly linked to the surface reflective properties. The detailed analysis of the albedo pattern from spaceborne observations provided reliable albedo values for dark and light roofs and for low albedo surfaces; different significant situations were investigated, and interesting results came up. The Landsat TM albedo succeeds on detecting dark and light roofs, as well as the high resolution IR thermography, but if the latter is averaged in a 30m grid (the same of the albedo), the most significant information gets lost. Besides, in the presence of low albedo materials, occasionally cold surfaces appeared in the airborne image due to the shadow of buildings, a circumstance that could bring to wrong evaluations of the surface reflective properties, trusting only on thermal images. Moreover, it is shown how the different exposures to the sun of the same surface affected the thermography results, a circumstance not found on Landsat TM albedo pattern.The paper demonstrates that satellite data show an effective potential for the assessment of the albedo of roofs and impervious surfaces, even when a high level of detail is required, providing information for improving urban planning efforts to dampen heating effects.

Fusion-based approach to change detection to reduce the effect of the trade-off parameter in the active contour model

This study proposes an approach to unsupervised change detection in which two different change maps are fused using different trade-off parameters of an active contour model. First, the change vector analysis method is conducted to produce a difference image from multitemporal and multispectral remotely sensed images. Second, two change maps are obtained based on the difference image using an active contour model using two different values of the trade-off parameter. Finally, an advantage fusion strategy is proposed to yield a final change map by fusing the two change maps, thereby reducing false alarms and preserving the accurate outlines of the changed regions. Two experiments are conducted with Landsat-7 Enhanced Thematic Mapper Plus and Landsat-5 Thematic Mapper data sets to evaluate the performance of the proposed method. Results confirm the effectiveness of the proposed approach vis-à-vis some of the state-of-the-art methods. This work contributes to the reduction of the effect of the trade-off parameter on the accuracy of the change map.

Spatial estimation of urban forest structures with Landsat TM data and field measurements

Urban forest structure is an important variable that influences urban forest ecosystem function across the landscape. However, it is generally labor-intensive and time-consuming to obtain urban forest structural attributes with the traditional field sampling methods. This study explores the potential of using Landsat-5 Thematic Mapper (TM) imagery in estimating urban forest structural attributes including stem density, diameter at breast height, tree height, leaf area index, canopy density and basal area. In our study, three vegetation indices including normalized difference vegetation index (NDVI), simple ratio (SR), and green normalized difference vegetation (GNDVI), obtained from Landsat-5 Thematic Mapper (TM) data and urban forest structure data derived from the field-based survey were used to develop a regression model to predict the selected urban forest structures in Changchun, China. Finally, an urban forest structure map was produced from the vegetation indices map by using the regression models based on measured urban forest structural attributes and vegetation indices. The results show that NDVI is better than SR and GNDVI for predicting the selected urban forest structural attributes. But some forest structure metrics that can be predicted well in natural forests by NDVI cannot be predicted for urban forests. Canopy density, basal area and leaf area index were strongly related to NDVI. Stem density, diameter at breast height and tree height were not related to NDVI. In Changchun city, leaf area index, canopy density and basal area of the urban forest all show a definite gradient decreasing from suburban areas to urban center areas. The canopy density, basal area and leaf area index class distribution were all skewed toward low values. The results demonstrate Landsat TM has a relatively rapid and efficient capability for quantitative estimation of some urban forest structural attributes including leaf area index, canopy density and basal area over urban areas.

Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data

Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively.

Effects of land use transitions due to rapid urbanization on ecosystem services: Implications for urban planning in the new developing area of China

With the rapid development of Chinese economy, many negative effects of land use transitions under the pressure of rapid urbanization on local ecological system and environment have occurred. This paper examines the dynamic patterns of land use in Tianjin Binhai New Area experienced rapid urbanization, using high-resolution Landsat TM (Thematic Mapper) data in 1985, 1995, 2005 and 2010, and socio-economic data from both research institutes and government departments, and assesses the changes of ecosystem services value (ESV) by drawing a connection between the observed land use dynamics and the evaluation of ESV, based on the latest research of Costanza et al. (2014) and some revisions adapted to the situation of China. The outcomes indicated that, during the period 1985–2010, ESV of the study area decreased by 25.9%, from 12,194 to 9037 billion RMB¥, due to the losses of large quantities of ecological land (e.g., farmland and water body) to construction land. Then, some of the major implications for improving the urban planning of Tianjin Binhai New Area were discussed. The authors argue that it is fundamental to meet the demand of construction land for socio-economic development, meanwhile protect regional ecosystem services function and maintain its stability; only in this way can the new developing area of China realize the sustainable use of ecological resources in the process of rapid urbanization, as well as the integration of economic, social and ecological benefits.

Delineation of groundwater potential zones in Araniar River basin, Tamil Nadu, India: an integrated remote sensing and geographical information system approach

The paper presents the development of a groundwater potential index (GWPI) map of the Araniar River basin, India, through an overlay analysis of climatic, geologic, geomorphic, soil and land use/land cover features of the basin using Landsat5 Thematic Mapper (TM) data and ArcGIS9.2. A correlation analysis of the developed GWPI map was carried out with a yield map of the basin to standardize the weights assigned to each theme. A maximum correlation coefficient of 0.922 was obtained when rainfall, geology, soil, slope, geomorphology and land use/land cover maps were overlaid with standardized weights of 0.49, 0.20, 0.17, 0.05, 0.05 and 0.04, respectively. The GWPI map showed groundwater potential zones as “excellent”, “very good”, “good”, “moderate” and “poor” with yield values in the ranges 293–361, 210–292, 126–209, 43–125 and 15–42 lpm, respectively. The analysis of post- and pre-monsoon water table depths also showed pronounced groundwater-level fluctuations in the central and eastern part of the basin. The GWPI map when combined with the post- and pre-monsoon water table depth maps may also help in locating suitable sites for artificial recharge for effective and efficient groundwater resource exploration and management of the basin.

Crop type identification by integration of high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results.

Surface soil moisture estimation over dense crop using Envisat ASAR and Landsat TM imagery: an approach

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m3 m−3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model.

Rural settlement land dynamic modes and policy implications in Beijing metropolitan region, China

Rapid urbanization has transformed the landscape and function of rural areas in the periphery of the metropolitan regions of China. As a consequence of urbanization, more than 900,000 Chinese villages have disappeared during the past decade. However, the landscape patterns and factors driving rural-urban conversion have long been neglected. Using medium-resolution Landsat TM (Thematic Mapper) data for 1978, 1988, 2000, 2005, and 2008, and socio-economic data, the spatio-temporal dynamic patterns of rural settlements in the Beijing metropolitan region were analyzed. During the past 30 years, Beijing has experienced a 33.6% increase in rural settlement land and a 34.8% decline in its rural population. The amount of rural settlement land per capita has increased dramatically. Following urban expansion, the major source of rural settlement land was cropland. Three modes of edge-expansion, dispersion, and urban encroachment were distinguished to study the dynamic pattern of rural settlements. Edge-expansion was the most common development type, and the proportion of land areas experiencing this dynamic mode was highest during the rapid expansion of rural settlement land. Rural settlement land initially developed around urban land, and then expanded to peripheral regions and remote villages. The expansion of rural settlement land and urban encroachment tended to be concentrated within 3 km of the urban center, and extended around the new urban development zone (NUDZ). This study investigated the dynamic modes of rural settlements and the drivers of a digital elevation model (DEM), such as distance to cropland, water bodies, roads and the urban center in the Beijing metropolitan region. The distance to water bodies had more impact on rural settlement land expansion than the distance to cropland. Urban expansion was influenced more by the distance to a road than the expansion of rural settlement land. To assist in the integration of urban citizens and farmers, the central government has implemented the “harmonious society”, “building a new countryside”, “scientific development” and “neo-urbanization” policies. In the Beijing metropolitan region, a scientific process of rural development planning is fundamental to reconsolidate the rural settlement land.

Mapping and assessing seagrass bed changes in Central Florida's west coast using multitemporal Landsat TM imagery

Some seagrass meadows in coastal shallow waters have displayed large scale changes in seagrass spatial extent and hurricanes and/or tropical storms have been suggested as factors responsible for reduction in coverage. Taking advantage of the incidence of three tropical storms passing near a study site along the central west Florida coast within a two-month period in 2004, we evaluated whether satellite remote sensing techniques (Landsat 5 Thematic Mapper (TM) imagery) are useful for assessing dynamics of seagrass (=submerged aquatic vegetation: SAV) cover/abundance in response to these multiple disturbances. We also examined whether an image preprocessing procedure, which included water column correction, applied to the Landsat TM images could further improve the classification and mapping of detailed SAV coverage. We compared a historical set of Landsat TM images, acquired in Fall 2003 and Fall and late Summer 2005, which were processed to classify %SAV cover into five classes using a maximum likelihood classifier. Importantly, our experimental results demonstrated that the application of the image preprocessing procedures led to an overall accuracy 2–14% improvement in SAV classification due to water column correction compared to that currently reported in the literature when similar Landsat TM data are utilized. Based upon the classification results mapped from the TM images and as well as a similar classification of SAV interpreted from aerial photographs collected before and after the passage of these same storms, SAV coverage over the study areas was found to increase about 6% (integrating SAV losses and gains) by 2005/2006 in comparison to cover levels present prior to the repeated storm activity. We conclude that heavy rains during 2004 along with physical disturbance from gale force winds from the tropical storms/hurricanes did not produce any SAV bed loss at the study site that was sustained for more than one year after multiple storm passage.

Estimation of the Relationship Between Urban Vegetation Configuration and Land Surface Temperature with Remote Sensing

Urban vegetation can help decrease Land surface temperature (LST) to mitigate urban heat island (UHI) effects. The relationship between LST and urban vegetation amount has been extensively documented. However, few studies have examined the relationship between LST and urban vegetation configuration and particularly whether the relationship changes across scales. In this study, LST in Changchun, China was obtained from Landsat-5 Thematic Mapper (TM) data and then correlated to urban vegetation amount and configuration information derived from high-spatial-resolution SPOT satellite data to uncover the relationship between urban vegetation configuration and LST. These results suggest that not only by increasing the amounts of urban vegetation, but also by optimizing their spatial pattern of urban vegetation can decrease LST. Given a fixed amount of urban vegetation, LST can be significantly decreased or increased by different configuration of urban vegetation. Besides the relationship between LST and urban vegetation configuration is complex and scale dependant and spatial scales should be considered when we try to explore the relationship between them. These findings can deepen the understanding of their interactions between LST and urban vegetation and provide useful information for urban planners about how to arrange urban vegetation at the landscape level to improve urban thermal environment.

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.

Built-up area extraction using Landsat 8 OLI imagery

The normalized difference built-up index (NDBI) has been useful for mapping urban built-up areas using Landsat Thematic Mapper (TM) data. The applicability of this index to the newer Landsat-8 Operational Land Imager (OLI) data was examined during this study, and a new method for built-up area extraction has been proposed. OLI imagery of urban areas of Lahore, Pakistan, was used to extract built-up areas through a modified NDBI approach and the proposed built-up area extraction method (BAEM). Instead of using individual bands, BAEM employed principal component analysis images of the highly correlated bands pertinent to NDBI computation. Through integration of temperature data, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), BAEM was able to improve the overall accuracy of built-up area extraction by 11.84% compared to the modified NDBI approach. Rather than employing the binary NDBI, NDVI and MNDWI images, continuous images of these indices were used, and the final output was recoded by determining the threshold value through a double-window flexible pace search (DFPS) method. Results indicate that BAEM was more accurate at mapping urban built-up areas when applied to OLI imagery as compared to the modified NDBI approach; omission and commission errors were reduced by 75.96% and 33.36%, respectively. Moreover, the use of DFPS improved robustness of the proposed approach by enhancing user control over the segmentation of the output.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Application of ASTER and Landsat TM Data for Geological Mapping of Esfandagheh Ophiolite Complex, Southern Iran

AbstractThis research developed an approach to enable the discrimination of lithological units and detection of host rock of chromitite bodies within ophiolitic complexes using the advanced spaceborne thermal emission and reflection radiometer (ASTER) and Landsat thematic mapper (TM) satellite data. Three main ophiolite complexes located in southern Iran were selected for the study. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, minimum noise fraction (MNF) components and spectral angle mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes. Minimum noise fraction components of ASTER and Landsat TM data are suitable for distinguishing ophiolitic rock complexes at a regional scale. The integration of SAM and feature level fusion used in this investigation discriminated the ophiolitic rock units and provided geological map for the study area, including identification of high potential areas (serpentinite dunite) for chromite exploration targets.

Mapping a burned forest area from Landsat TM data by multiple methods

Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour- and time-consuming, and more repeatable.

Change Detection through Four Techniques Using Multi-Temporal Landsat Thematic Mapper Data: A Case Study on Falavarjan Area, Isfahan, Iran

The present study was aimed to assess the applicability of four techniques for detecting changed and unchanged areas in terms of land cover in Falavarjan area, (Isfahan, Iran). The images of the multi-temporal Landsat Thematic Mapper (TM) data acquired on 17 September, 1990 and 13 August, 2010 were used to apply land cover change analysis. The Images were respectively radiometrically and geometrically corrected. The root mean square errors were less than 0.5 pixels for each image. Finally, the image differencing method was used to produce the change image. To separate out the changed and unchanged areas in the difference image, four techniques including Metternicht's method, statistical method, Liu's method and Kapur's method were employed. Among them, the Metternicht's method followed by statistical thresholding technique yielded more accurate binary images.

Empirical relationships for monitoring water quality of lakes and reservoirs through multispectral images

Remote sensing techniques can be used to estimate water quality variables such as chlorophyll\mbi a, total suspended particles, and water transparency. This paper describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. Ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were the Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll\mbi a from the ratio in reflectance values between bands 2 and 4 of TM ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> \mmb = 0.66, p\lt 0.001), transparency [Secchi disk (SD)] from reflectance in band 2 ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> \mmb = 0.80, pbf \lt 0.001), and total suspended particles from reflectance in band 4 ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> \mmb = 0.92, p\lt 0.001). The spectral equivalence between TM and the recent satellite Deimos-1 was also tested. By applying the proposed algorithms to this new sensor, the temporal resolution is improved by up to 3 days, which increases spatial resolution to 22 m. The algorithms were validated using three Deimos-1 scenes of the Albufera de Valencia together with ground measurements. Results of this validation showed root-mean-square errors (RMSEs) of 40\nbspmg·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\mmb-3</sup> for Chl-\mbi a (data range: 32\mmb - 238\nbspmg·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> ), 10\nbspmg·L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\mmb -1</sup> for total suspended solid (TSS) (data range: 25\mmb -89\nbspmg·L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\mmb -1</sup> ), and 0.10 m for SD (data range: 0.17-0.40 m). In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to three decades of TM imagery.

Global and local modeling of soil organic carbon using Thematic Mapper data in a semi-arid environment

This study aimed at comparing the effectiveness of the global aspatial multiple linear regression (MLR) using ordinary least squares (OLS) and local spatial geographically weighted regression (GWR) for producing a map showing the spatial variations in soil organic carbon (SOC) in Amman-Zarqa Basin (about 3,583 km2)—a typical semi-arid watershed in Jordan—using Landsat Thematic Mapper (TM) data. After normalizing the SOC data (the dependent variable) using Box-Cox power transformation and removing the multicollinearity of TM bands 1 to 5 and 7 (the independent variables) by applying principal component analysis, both regression techniques developed maximum likelihood best linear unbiased estimators in which the residuals had close-to-normal and random independent distributions with almost common variances and close-to-zero means. However, the GWR model had smaller Akaike’s information criterion (corrected) (AICc) (2,534.0 versus 2,560.5), larger adjusted multiple coefficient of determination \( \left({\overline{\mathbf{\mathcal{R}}}}^2\right) \) (0.31 versus 0.22), and larger Pearson’s product moment correlation coefficient (r) between measured and observed values (0.63 versus 0.51). Thus, applying map algebra using the developed GWR model generated a raster map with 30 × 30 m2 cell size. The map showed that SOC composition to 20 cm depth varied from 3.5 to 85.0 metric tons per hectare (ton/ha) with a mean and standard deviation of about 23.9 and 9.3 ton/ha, respectively. The spatial pattern of surface SOC reflected partly the spatial variability of land cover and agricultural management practices in the basin. The results demonstrate the potential and superiority of GWR over MLR as a practical tool for conducting further spatial and temporal analyses of SOC stocks and implementing best land management practices in semi-arid environments using TM data.