Landsat Thematic Mapper Images Research Articles

On-land manifestation of along-strike transitioning of the Red Sea from continental rifting to sea floor spreading

This study uses Landsat Thematic Mapper (TM), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and aeromagnetic data to examine possible reactivation of the Ediacaran, NE-trending Ad Damm Fault Zone (AFZ) and the Fatima Fault Zone (FFZ) in the western Arabian Shield. Reactivation is by latest Paleogene to Neogene and Quaternary structures of the Red Sea. The AFZ is the tectonic boundary between the Tonian Jeddah terrane in the northwest and the Asir terrane to the southeast of the Arabian Shield. The AFZ and FFZ coincide with possible onshore continuation of a transform fault that separates the sea floor spreading segment of the Red Sea in the south from its transitional segment to the north. It has been suggested that the AFZ is a candidate for the formation of a future onshore transform fault that would result in an eastward jump of the Red Sea sea floor spreading center onto the Arabian Shield. The future spreading center will follow the latest Neogene to Quaternary volcanic rocks (Harrat) that are exposed on the eastern side of the Red Sea. The Landsat TM image and the SRTM DEM show that the AFZ dissects the Red Sea topographic escarpment, and both the AFZ and FFZ control paleo-drainage system (wadis). The aeromagnetic data show that there is a ∼100 km wide zone between the AFZ and FFZ dominated by NE-trending short wavelength magnetic anomalies, possibly related to regional tectonic fabric. Additionally, the aeromagnetic data show that both fault zones dissect NW-trending short wavelength magnetic anomalies, interpreted as latest Paleogene to earliest Neogene dike swarms associated with the opening of the Red Sea. This dissection also includes possible Red Sea related narrow marginal grabens and horsts that were formed onshore and offshore parallel to the Red Sea shoreline. Red Sea-related structure might have reactivated the AFZ and FFZ.

Impact of Changes in Land use and land cover on Agricultural Productivity in Northern Nigeria

The challenges posed by changes in land use and land cover are greatly affecting agricultural productivity in the semi-arid region of Nigeria. Land use and land cover change affects ecosystem functions and services, which leads to people migrating from one place to another, predominantly rural dwellers. The research studies the impact of land use and land cover change on agricultural productivity and the factors influencing the changes. To achieve this, a remote sensing technique was employed. The Landsat Thematic Mapper Image of 1990, Landsat Enhanced Thematic Mapper Image of 2005 and Landsat Enhanced Thematic Mapper Image of 2020 were collected and analysed. Six classes of land use and land cover were obtained using Earth Resource Development Assessment System Imagery 9.1 software. The result shows that there was a decrease in the land area occupied by wetland, shrubland and water body, while there was an increase in the land area occupied by bare lands/dunes, settlement and scattered cultivation from the year 1990 to 2020. Increase in bare land/dune, scattered cultivation and settlement were mainly as a result of climate change resulting from overgrazing, a southward movement of dunes from the Sahara desert, increasing farming activities and increasing demand for shelter by the increasing population. The decrease in shrubland, water body, and wetland were also linked to climate change resulting from increasing deforestation, and increasing use of wetland for farming activities. Based on the results of the analysis, it is recommended that measures should be taken to integrate sustainable land management and climate change adaptation options for sustainable Agriculture.

Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales.

The increasing availability of satellite imagery has supported a rapid expansion in forward-looking studies seeking to track and predict how climate change will influence wild population dynamics. However, these data can also be used in retrospect to provide additional context for historical data in the absence of contemporaneous environmental measurements. We used 167 Landsat-5 Thematic Mapper (TM) images spanning 13 years to identify environmental drivers of fitness and population size in a well-characterized population of banner-tailed kangaroo rats (Dipodomys spectabilis) in the southwestern United States. We found evidence of two decoupled processes that may be driving population dynamics in opposing directions over distinct time frames. Specifically, increasing mean surface temperature corresponded to increased individual fitness, where fitness is defined as the number of offspring produced by a single individual. This result contrasts with our findings for population size, where increasing surface temperature led to decreased numbers of active mounds. These relationships between surface temperature and (i) individual fitness and (ii) population size would not have been identified in the absence of remotely sensed data, indicating that such information can be used to test existing hypotheses and generate new ecological predictions regarding fitness at multiple spatial scales and degrees of sampling effort. To our knowledge, this study is the first to directly link remotely sensed environmental data to individual fitness in a nearly exhaustively sampled population, opening a new avenue for incorporating remote sensing data into eco-evolutionary studies.

Multi-temporal assessment of land surface temperatures as an indicator of land use/cover changes and climate variability in the Develi Basin, Turkey.

Land surface temperature (LST) is an important parameter that reflects land surface processes of water and energy balance and has been used in assessment of land use/cover changes. However, the use of LST in monitoring changes in non-urban areas such as agricultural areas and wetlands is still limited. In this study, we aim to determine the spatial and temporal changes in LST in a semi-arid agricultural basin in Turkey (Develi Basin), where land use/cover and climatic conditions showed considerable variability since 1980s. Irrigated agriculture have expanded in the basin since 1987, after the construction of a large irrigation project. The basin hosts an internationally important wetland, called the Sultan Marshes, affected negatively by irrigation expansion. The study covers a 39-year period from 1984 to 2022. Four Landsat Thematic Mapper (TM) images acquired in 1984, 1987, 2003, and 2007 and two Landsat 8 OLI/TIRS images acquired in 2014 and 2022 were used in the analyses. The land use/cover changes were evaluated based on Normalized Difference Vegetation Index (NDVI). LST was estimated through top-of-atmosphere brightness temperature from thermal bands of Landsat images. Climate variability from 2014 to 2022 was analyzed with statistical methods. The results indicated that Develi Basin faced both spatial and temporal land use/cover changes. The area covered with natural steppe vegetation and water bodies decreased in the basin. In contrast, the sparsely and densely vegetated soil covers, which mostly denote agricultural areas, increased. Changes in LST values were observed from 1984 to 2022 as a result of climatic factors and land use/cover changes. LST changes were variable across different land use/cover types; LST decreased in irrigated areas and increased in lakes that went dry over years. LST changes proved useful for evaluating land use/cover changes and climatic variations in agricultural basins.

Mapping and Monitoring Forest Plantations in São Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas’ rotation, especially the eucalypt’s short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations’ characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales.

Statistical analysis for determination of homogeneous reflectance regions in a coastal lagoon using satellite imagery and bathymetry survey

Coastal lagoons are highly productive ecosystems that offer a range of natural services for the environment and society. However, anthropic pressure in their basins cause water quality loss and endangers their ecosystem. Monitoring water quality constituent by remote sensing can provide spatial and high temporal resolution analysis. Not recognizing how depth influences on the signal received by a sensor causes on significant errors in estimating water constituent’s concentrations. Using a bathymetry survey and Landsat-5 thematic mapper (TM) images, different patterns of depth influence on reflectance were recognized. By means of a statistical analysis, it was able to classify and analyze three different regions of homogeneous spectral behavior for the closest image from the bathymetric survey and for a median image from 2001 to 2011. Nonparametric Kruskal–Wallis test and Dunn’s post hoc test provided great values in differentiating all groups generated for the coastal lagoon to both closest and median image. The methodology proposed here, being based on statistical methods, can be applied with the use of other images to assist studies in coastal lagoons across the globe.

Comparison of Impervious Surface Dynamics through Vegetation/High-Albedo/Low-Albedo/Soil Model and Socio-Economic Factors

Hong Kong and Shenzhen have entirely different land-use development policies, resulting in a disparity in the increase rate of impervious surface area. Impervious surface estimation is a significant method for evaluating urbanization, so that countries and cities can deal with their growing populations. The impervious surface area was estimated through Landsat Thematic Mapper (TM) image extraction, the V-H-L-S (vegetation, high-albedo, low-albedo, and soil) model, and linear spectral un-mixing analysis (LSUM). Changes in fractions of endmembers over periods of time were identified and employed to analyze changes in land use and land cover (LULC). The research adopting the V-H-L-S model for classifying land cover and exploring the association of change in impervious surface areas and socio-economic growth over a period of time is limited. In this study, impervious surface estimations for Hong Kong and Shenzhen in 1995, 2005, and 2016 were compared, selecting vegetation, high-albedo, low-albedo, and soil as endmembers. The change rate of the fractions in the four endmembers was calculated to identify changes in land use and land cover during these three specific time periods. The impervious surface was determined to constitute a combination of high-albedo and low-albedo. Moreover, a proportional relationship exists between the increase in impervious surface area, population rate, GDP, and GDP per capita in both Hong Kong and Shenzhen. However, there was a difference in the increase in impervious surface area between Hong Kong and Shenzhen due to the different land-use policies in the country’s two systems.

Evaluating the Impact of the Influx of Syrian Refugees on Land Use/Land Cover Change in Irbid District, Northwestern Jordan

The refugee movement creates urban and environmental pressures at their destination locations. This pressure often presents in the form of Land Use/Land Cover (LULC) change. This study seeks to understand the impact of the Syrian refugees’ influence on changing the urban and agricultural land dynamics in Irbid district in northwestern Jordan from 1985 to 2021, including the period of the civil war in Syria, using Landsat Thematic Mapper (TM) images for the years 1985 and 2004, and the Landsat-8 Operational Land Imager (OLI) for the years 2013 and 2021. The Google Earth Engine (GEE) platform was used to conduct all image processing and perform calculations and classification analysis using the Random Forest (RF) approach. The study of the classified images compared LULC before and during the Syrian crisis using images from 1985, 2004, 2013, and 2021. The results show that the urban area increased. In parallel, agricultural land increased. During the Syrian refugee crisis, agriculture became a significant livelihood activity for Syrian refugees. In summary, the movement of the refugees to Irbid district caused an increased demand for land and housing, which accelerated the building and construction process.

Identification of Hazardous Glacial Lakes in the Yarlung Zangbo River Basin Based on Lakes Changes Determined Using Google Earth Engine

Glaciers have melted rapidly in the Yarlung Zangbo River Basin due to recent climate change, which has accelerated the expansion of glacial lakes. We performed a systematic survey of glacial lakes in the Yarlung Zangbo River Basin using the Google Earth Engine platform. This allowed us to map the distribution and monitor the evolution of glacial lakes over seven time periods from 1990 to 2020, using more than 140 Landsat Thematic Mapper and Operational Land Imager images at a 30-m resolution. The results show that there were 2088 glacial lakes in 2020, with a total area of 245.87 km2, mainly distributed at elevations of 3,800–5,800 m above sea level. From 1990 to 2020, the Yarlung Zangbo River Basin glacial lakes expanded by approximately 1.81 km2·a−1. A total of 153 glacial lakes in the Yarlung Zangbo River Basin were assessed. The assessment shows that 153 glacial lakes were divided into very high, high, medium, low, and very low level. The number of glacial lakes in each level is 23, 22, 32, 32, and 44, respectively. The very high and high level glacial lakes were mainly found in the Yi’ong Zangbo River Basin, Parlung Zangbo River Basin, the western of the Yarlung Zangbo River Basin and Nianchu River Basin. These glacial lakes deserve further attention and monitoring.

The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research

Abstract. A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at https://doi.org/10.11888/Snow.tpdc.271381 (Hao et al., 2021).

Evaluating the Applicability of Thermal Infrared Remote Sensing in Estimating Water Potential of the Karst Aquifer: A Case Study in North Adriatic, Croatia

One of the most prominent tourist destinations in the Adriatic coast, the city of Opatija, is facing a problem concerning seasonal drinking water shortages. The existing water resources are no longer sufficient, and attention is being given to alternative resources such as the underlying karstic aquifer and several coastal springs in the city itself. However, the water potential of the area still cannot be estimated due to the insufficient hydrological data. The goal of this research was to evaluate the use of thermal infrared (TIR) remote sensing as the source of valuable information that will improve our understanding of the groundwater discharge dynamics. Ten Landsat ETM+ (enhanced thematic mapper plus) and two Landsat TM (thematic mapper) images of the north Adriatic, recorded during 1999–2004 at the same time as the field discharge measurements, were used to derive sea surface temperature (SST) and to analyze freshwater outflows seen as the thermal anomaly in the TIR images. The approach is based on finding the functional relationship between the size of the freshwater thermal signatures and the measured discharge data, and to estimate the water potential of the underlying aquifer. It also involved analyzing the possible connection between the adjusted size of the spring’s thermal signatures and groundwater level fluctuations in the deeper karst hinterland. The proposed methodology resulted in realistic discharge estimates, as well as a good fit between thermal anomalies with measured discharges and the groundwater level. It should be emphasized that the results are site specific and based on a limited data set. However, they confirm that the proposed method can provide additional information on groundwater outflow dynamics and coastal springs’ freshwater quantification.

Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

Change Detection Method with Multi-temporal Satellite Images based on Wavelet Decomposition and Tiling

Change detection method with multi-temporal satellite images based on Wavelet decomposition with Daubechies wavelet function (Multi Resolution Analysis), and tiling is proposed. The method allows detection of changes in time series analysis and is not sensitive to geometric distortions included in the satellite images. In this paper, the author proposed a method based on MRA as a method for extracting change points from satellite images acquired over many periods. Change detection method with multi-temporal satellite images based on Wavelet decomposition and tiling is proposed. The method allows to detect changes and is not sensitive to geometric distortions included in the satellite images. The experimental results with simulation image and a Landsat Thematic Mapper (TM) image show that more appropriate changes can be detected with the proposed method in comparison with the existing method of subtraction. When applied to simulations and real satellite images, it was confirmed that they were robust to minute nonlinear geometric distortion.

Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions

A number of methods are available for topographic correction of remote sensing data, and various approaches have been proposed for the evaluation of topographic correction effects. However, few studies have evaluated the topographic correction effects for images obtained under all possible sun elevation conditions. This study employed three correction methods, namely, Statistical-Empirical (SE), C-correction (C), and the Sun-Canopy-Sensor + C-correction (SCS + C) methods. These were used for topographic correction of 11 images obtained by the Landsat-5 Thematic Mapper sensor from June to December. An evaluation of the effectiveness of these three methods was carried out based on five evaluation indices, with the goal of investigating the variation in the topographic correction effect with changes in lighting conditions and the adaptability of topographic correction methods to those conditions. The results showed that the SE method effectively reduced the dependence of surface reflectance on the terrain when the solar elevation was greater than 46°, yielding a very stable surface reflectance and a low proportion of outliers. At sun elevation below 46°, the SCS + C method has good correction effect. After topographic correction using these three methods, the correction effect was compromised as the lighting conditions weakened. Within areas where the topographic and geographical conditions are similar to that of this study, the correction effect can be divided into three levels based on the solar elevation while obtaining the images, i.e., > 60°, 50–60° and < 50°. Caution should be taken when conducting quantitative research on images obtained when the solar elevation is < 50°.

Spatio-temporal dynamics in seismic exposure of Asian megacities: past, present and future

The estimation of urban growth in megacities is a critical and intricate task for researchers and decision-makers owing to the complexity of these urban systems. Currently, the majority of megacities are located in Asia which is one of the most disaster-prone regions in the world. The high concentrations of people, infrastructure and assets in megacities create high loss potentials for natural hazards; therefore, the forecasting of exposure metrics such as built-up area is crucial for disaster risk assessment. This study aims to identify and project the dynamics of built-up area at risk using a spatio-temporal approach considering seismic hazard in three Asian megacities, namely Jakarta, Metro Manila and Istanbul. First, Landsat Thematic Mapper images were processed to obtain the built-up areas of 1995 and 2016 for Metro Manila, and of 1995 and 2018 for Jakarta and Istanbul. The SLEUTH urban growth model, a cellular automaton (CA)-based spatial model that simulates urban growth using historical geospatial data, was then employed to predict the urban growth of these megacities by 2030. Finally, seismic hazard maps obtained for 10% and 2% probabilities of exceedance were overlaid with built-up area maps. For a seismic hazard of 10% probability of exceedance in 50 years, the total urban area subjected to Modified Mercalli intensities (MMI) VIII and IX has increased nearly 65% over 35 years in Metro Manila. For Jakarta and Istanbul, the total urban area at the MMI VIII level has increased nearly 79% and 54% over 35 years, respectively. For a seismic hazard of 2% probability of exceedance in 50 years, the total urban area subjected to MMI IX has increased nearly 75%, 65% and 49% over 35 years in Jakarta, Metro Manila and Istanbul, respectively. The results show that urban growth modelling can be utilized to assess the built-up area exposed to high risk as well as to plan urban growth considering natural hazards in megacities.

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

A serious earthquake could trigger thousands of landslides and produce some slopes more sensitive to slide in future. Landslides could threaten human’s lives and properties, and thus mapping the post-earthquake landslide susceptibility is very valuable for a rapid response to landslide disasters in terms of relief resource allocation and posterior earthquake reconstruction. Previous researchers have proposed many methods to map landslide susceptibility but seldom considered the spatial structure information of the factors that influence a slide. In this study, we first developed a U-net like model suitable for mapping post-earthquake landslide susceptibility. The post-earthquake high spatial airborne images were used for producing a landslide inventory. Pre-earthquake Landsat TM (Thematic Mapper) images and the influencing factors such as digital elevation model (DEM), slope, aspect, multi-scale topographic position index (mTPI), lithology, fault, road network, streams network, and macroseismic intensity (MI) were prepared as the input layers of the model. Application of the model to the heavy-hit area of the destructive 2008 Wenchuan earthquake resulted in a high validation accuracy (precision 0.77, recall 0.90, F1 score 0.83, and AUC 0.90). The performance of this U-net like model was also compared with those of traditional logistic regression (LR) and support vector machine (SVM) models on both the model area and independent testing area with the former being stronger than the two traditional models. The U-net like model introduced in this paper provides us the inspiration that balancing the environmental influence of a pixel itself and its surrounding pixels to perform a better landslide susceptibility mapping (LSM) task is useful and feasible when using remote sensing and GIS technology.

High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction.

Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the “pooling” process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction.

Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines

The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra–Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management.

Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images

Soil organic carbon (SOC) is the largest carbon pool and a key property of ecosystems. Compared with traditional field surveys, remote sensing (RS) represents a more efficient approach to mapping SOC, especially in larger-scale areas. Hyperspectral imagery provides a great potential for SOC prediction, but predicting the SOC content at a regional scale for a given year remains a great challenge. Multispectral RS images (e.g., Landsat images) with middle spatial resolution can be used as an alternative to predict SOC amongst other images. However, multispectral images are commonly affected by cloud cover and lack information on the shallow soil surface and soil surface with vegetation. By contrast, synthetic aperture radar (SAR) images can capture shallow surface information, penetrate through vegetation and are not affected by cloud cover. In this study, a new approach was evaluated for the prediction of SOC content by integrating Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) images. Mainland Spain (hereafter Spain) was used as the study site. Soil samples from the Land Use and Coverage Area frame Survey (LUCAS) European topsoil dataset in 2009 were utilized to fit a random forest (RF) model of the relationships between SOC content and several covariates in Spain. SOC prediction using only the Landsat TM image and only the ALOS PALSAR image was established as benchmarks to verify the efficiency of the proposed approach. The results for Spain showed that the integrated approach holds the highest prediction accuracy, with R2, RPD and RMSE of 0.59, 1.98 and 9.27 g kg−1, respectively. The correlations between the SOC content and various covariates were investigated and discussed, and the derived indices presented high relationships with SOC. The proposed method can accurately map the SOC content covering a large area by combining spectral information on land cover from the Landsat TM and topsoil information from the ALOS PALSAR. Land covers and elevation are closely related to SOC prediction, where lands with high vegetation canopy density have high SOC content, and the SOC content in areas with slopes within 15° is mainly concentrated between 3 and 26 g kg−1.

Climate Change May Increase the Drought Stress of Mesophytic Trees Downslope With Ongoing Forest Mesophication Under a History of Fire Suppression

In mountainous headwater catchments, downslope flow of subsurface water could buffer downslope forest communities from soil moisture stress during drought. Here we investigated changes in landscape-scale vegetation patterns at five forested headwater catchments in the Coweeta Hydrologic Laboratory in the southern Appalachians. We used a ca. 30-year Landsat Thematic Mapper (TM) image record of normalized difference vegetation index (NDVI), spanning a period of recorded warming since the mid-1970. We then, related spatial and temporal canopy patterns to seasonal water balance, streamflow recession behavior, and low flow dynamics from the long-term hydrologic records. All hydrologic metrics indicated increasing evapotranspiration, decreasing streamflow given precipitation, and potentially decreasing downslope subsidy at the watershed scale over time, especially during low-flow periods. Contrary to expectations, leaf area index (LAI) and basal area increased more upslope compared to downslope over time, coincident with warming. Trends in the ratio of NDVI in upslope and downslope topographic positions were also supported by long-term tree basal area increment, litterfall, and sap flux data in one of the reference watersheds. Mesophytic trees downslope appeared to respond more to frequent droughts and experience lower growth than xerophytic trees upslope, closely mediated by the isohydric/anisohydric continuum along hydrologic flow paths. Considering ongoing forest “mesophication” under a history of fire suppression across the eastern US deciduous forests, this study suggests that mesophytic trees downslope may be more vulnerable than xerophytic trees upslope under ongoing climate change due to an apparent dependence on upslope water subsidy.