Remote-sensing Methods Research Articles

Integrating process-related information into an artificial neural network for root-zone soil moisture prediction

Abstract. Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and the soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote-sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-related inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporation efficiency computed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing dataset and a simplified analytical model, while vegetation growth was not modeled and was only inferred through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed in several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data-hosting facility. The results indicate that the integration of process-related features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semiarid areas, for instance, performance enhancement was observed when the evaporation efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied to observation sites in Tunisia, Italy and southern India that are not part of the ISMN. The results reveal that joint use of surface soil moisture, evaporation efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-related features were integrated into the ANN models in addition to SSM. However, process-related features have no to little added value in temperate to tropical conditions.

Comparison of Remote Sensing Methods for Plant Heights in Agricultural Fields Using Unmanned Aerial Vehicle-Based Structure From Motion.

Remote sensing using unmanned aerial vehicles (UAVs) and structure from motion (SfM) is useful for the sustainable and cost-effective management of agricultural fields. Ground control points (GCPs) are typically used for the high-precision monitoring of plant height (PH). Additionally, a secondary UAV flight is necessary when off-season images are processed to obtain the ground altitude (GA). In this study, four variables, namely, camera angles, real-time kinematic (RTK), GCPs, and methods for GA, were compared with the predictive performance of maize PH. Linear regression models for PH prediction were validated using training data from different targets on different flights (“different-targets-and-different-flight” cross-validation). PH prediction using UAV-SfM at a camera angle of –60° with RTK, GCPs, and GA obtained from an off-season flight scored a high coefficient of determination and a low mean absolute error (MAE) for validation data (R2val = 0.766, MAE = 0.039 m in the vegetative stage; R2val = 0.803, MAE = 0.063 m in the reproductive stage). The low-cost case (LC) method, conducted at a camera angle of –60° without RTK, GCPs, or an extra off-season flight, achieved comparable predictive performance (R2val = 0.794, MAE = 0.036 m in the vegetative stage; R2val = 0.749, MAE = 0.072 m in the reproductive stage), suggesting that this method can achieve low-cost and high-precision PH monitoring.

Investigating the Relationship between Drought and Water Quality Reduction Using Remote Sensing and Neural Network Methods

با توجه به تأثیر خشکسالی در کیفیت و کمّیت آب، هدف از این مطالعه بررسی خشکسالی با استفاده از شاخص‌های خشکسالی و ارتباط آن با میزان کیفیت آب در مناطق شمالی استان فارس ایران است. برای این منظور، شاخص‌های خشکسالی PCI، TVDI، NDVI در سال‌های 2000 تا 2020 استفاده شد. در ادامه، نقشه‌های پهنه‌بندی عناصر آب (Ca، Cl، EC، K، Na، Mg) با استفاده از روش کریجینگ تولید شد. سپس با به‌کارگیری روش شبکه‌های عصبی (MLP)، میزان عناصر آب با استفاده از شاخص‌های خشکسالی پیش‌بینی شد. نتایج نشان داد که با توجه به مقادیر شاخص‌های خشکسالی، روند تغییرات خشکسالی در منطقه از سال 2000 تا 2020 افزایشی بوده و بخش‌های جنوبی منطقه در وضعیت حادتری به‌نسبت دیگر بخش‌ها قرار دارد. نتایج حاصل از نقشه‌های پهنه‌بندی عناصر آب هم نشان داد که در بخش‌های جنوبی، غلظت املاح بیشتر از بخش‌های شمالی است. طبق نتایج حاصل از همبستگی بین شاخص‌های خشکسالی و مقادیر عناصر آب، Ca همبستگی بالایی (820/0 R=) با شاخص TVDI دارد و عناصر Cl، EC، K، Na، Mg نیز دارای همبستگی معنی‌داری (80/0 R>) با شاخص PCI است. نتایج حاصل از روش MLP، برای پیش‌بینی وضعیت کیفیت آب با استفاده از شاخص‌های خشکسالی، نشان داد که در مناطق جنوبی میزان املاح بیشتر و در نتیجه، کیفیت آب کمتر است. میزان دقت مدل در پیش‌بینی عناصر Cl، EC، K، Na، Mg، TH،TDS با استفاده از شاخص PCI برابر با 85/0 R2= و درمورد عنصر Ca، با استفاده از شاخص TVDI برابر با 71/0 R2= است.

Development of different hydrogeological influencing thematic layers and groundwater potential zones of Deo river watershed of Mayurbhanj district, Odisha, India

Water is one of the most abundant renewable resources, and it is essential to all living things. Groundwater is one of the major sources of Water in the planet Earth. Continuous uses of water without any conservation cause lowering of Ground water level and thus living forms are facing many problems now-a-days. Due to increase in industrialisation in the state of Odisha water problem is rising its head. Using Remote Sensing and Geographic information system (GIS) methods, this research aids in the delineation of water potential zones in the Deo river watershed in Mayurbhanj district, odisha. For this, different thematic layers have been prepared and interpreted. This study may be helpful to monitor and conserve groundwater in near future.

Severe Drought Monitoring by Remote Sensing Methods and Its Impact on Wetlands Birds Assemblages in Nuntași and Tuzla Lakes (Danube Delta Biosphere Reserve)

The present paper aims to highlight the impact of the partial or total drying of the Nuntași and Tuzla lakes (from the Danube Delta Biosphere Reserve) as a result of intense drought phenomena on groups of waterfowl that are encountered in this region. Our analysis combined satellite remote sensing techniques with bird observations that were made monthly during the analyzed period, corroborated with the meteorological context of the time interval that was taken into account. The results of the satellite image processing show a partial drying in 2013 and a total drying in 2020 of the Nuntași and Tuzla lakes, which were caused by both natural factors (drought) and anthropogenic factors (inadequate management of the area—e.g.,: communication channels with surrounding lakes are clogged). These situations have led to repercussions for groups of birds, which behave differently depending on their ecology. Pelicans and swans are the most affected birds, they leave the area in the absence of water, whereas gulls and terns are not affected by the decrease in the water surface, they even increase their numbers in such conditions. Our study also shows that from 2010 to 2020 the largest numbers of birds (total numbers of birds), with the exception of pelicans, were recorded in 2013 and 2020, more precisely in the years when the water surface decreased considerably. Another important feature of this paper involves highlighting how fragile an ecosystem can be in the context of climate change, but also how important it is to involve human society in maintaining the adequate conditions for an ecosystem that is part of one of the most important biodiversity hotspots on the planet, the Danube Delta Biosphere Reserve.

Hyperspectral Image Classification Using Deep Learning Model

In today’s scenario, the classification process is required for numerous purposes, including planning of modern cities, agriculture production, and monitoring of environmental factors. So far, the optimal categorization level has remained unattainable. Manual feature-based approaches, unsupervised feature learning methods, and supervised feature learning methods are the three types of remote-sensing images classification methods available. Supervised deep learning methods have recently been widely used in a variety of remote sensing applications, including as object detection and land use scene classification. In contrast to traditional computer vision tasks that only use the spatial context, this article can improve hyperspectral image classification by utilizing both spatial context and spectral correlation. To achieve this, the benchmark hyperspectral dataset with CNN deep learning model for hyperspectral image classification is taken and analyzed for different learning rates for SGD optimizer. The accuracy percentage were maximum when the SGD optimizer were trained with maximum learning rate. As the learning rate decreases, so does the accuracy percentage.

Application of a remote-sensing three-source energy balance model to improve evapotranspiration partitioning in vineyards

Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California’s Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB’s latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m2 compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB’s modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management.

Using regional characteristics to improve uncalibrated estimation of rootzone soil moisture from optical/thermal remote-sensing

Remote-sensing methods based on optical and thermal satellite imagery have been proposed to estimate fine-resolution (30 m) rootzone soil moisture θ¯. In these methods, θ¯ is most commonly estimated using a single empirical relationship with evaporative fraction ΛSEB or evaporative index ΛPET. Methods have been proposed recently to estimate these relationships based on regional climate, soil, and vegetation characteristics, but those methods have not yet been applied to estimate θ¯ from remote sensing data. The objective of this study is to evaluate the θ¯ estimates from remote sensing when the ΛSEBvs.θ¯ and ΛPETvs.θ¯ relationships are inferred from regional characteristics using the previously proposed methods. Four study regions are considered including the Walnut Gulch Experimental Watershed in Arizona, the Piñon Canyon Maneuver Site and Lower Arkansas River Valley in Colorado, the Little Washita and Fort Cobb Experimental Watersheds in Oklahoma, and the Mississippi Delta region in Mississippi. The θ¯ estimates from the regionally adapted relationships are compared to θ¯ estimates from the single empirical relationship and to in situ θ¯ measurements. The estimates from the regionally adapted relationships consistently outperform the estimates form the single empirical relationship. The performance typically improves as more regional information is used in the relationships and reduces the root mean squared error of θ¯ by an average of 45% among the four regions. The regional method typically performs better for the arid and semiarid regions with root mean squared errors of 0.05 cm3cm−3 and 0.04 cm3cm−3, respectively. The regionally adapted relationships better capture both spatial and temporal variations of soil moisture than the single empirical relationship.

Remote-sensing monitoring of rubber plantations using object-oriented characteristics from the vigorous period

Natural rubber is one of the four major industrial raw materials in China, and the demand for it is increasing rapidly in China. However, due to geographic and climatic limitations, the rate of natural rubber production in China is significantly less than required to satisfy this demand. Therefore, to ensure the healthy development of China’s rubber industry, it is urgent to develop a method to rapidly and accurately monitor the planting and distribution of rubber forests in China. Existing studies have exploited the unique phenological characteristics of rubber plantations manifested by the spectral characteristics of the vigorous growth period and the deciduous period to identify rubber plantations through remote sensing. Unfortunately, the cloudy and rainy climate of rubber-growing regions makes it is difficult to obtain remote-sensing data in the optimal period. Therefore, the present study uses Planet images as the basic data and extracts typical spectral and textural features of rubber forests during the single vigorous growth period to construct a remote-sensing method to monitor rubber forests based on the object-oriented random forest algorithm, which uses all the spectral and texture features. The results show that the proposed random forest classification model achieves a high classification accuracy: the total classification accuracy reaches 89%, and the rubber producer’s accuracy and user’s accuracy both exceed 92%. This study can effectively solve the problem of lack image data with cloudless from deciduous period and provides a good theoretical basis for remote-sensing monitoring of China’s rubber forests, thereby facilitating the development of China’s rubber industry.

Use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics along non-perennial rivers in semi-arid environments, South Africa

This study explored the use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics in two distinct semi-arid sites in South Africa. The factors that control the pool dynamics were also examined. Three water extraction indices were used, these included Normalised Difference Water Index (NDWI), Modified NDWI and Normalised Difference Vegetation Index. In addition, random forest classifier and Sentinel-1 SAR data were used in mapping pools and pools dynamics for both sites. Overall, the remotely-sensed methods detected and mapped pools with acceptable accuracy, except for small pools (<400 m2). The results suggest that flow occurrences and rainfall are key in controlling temporal changes in pools sizes, and there was no interaction between pools and groundwater. The study showed that remote sensing methods are essential for filling ground monitoring gaps in non-perennial rivers and determining hydrological processes and water availability from pools in semi-arid environments.

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

Spectral Emissivity of Phonolite Lava at High Temperature

The rheology and thermodynamical evolution of magma, either in reservoirs, conduits, or at the surface, are governed by temperature. To determine the field temperature, remote-sensing methods based on measuring the infrared radiance are widely applied, but they are subject to assumptions and caveats that can propagate into large uncertainties. This is related to the poor knowledge of one of the most critical parameters, namely the spectral emissivity. In this work, we aim at filling this gap through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> spectral emissivity measurements performed over wide temperature (700–1600 K) and spectral ranges (1.25– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25~\mu \text{m}$ </tex-math></inline-formula> ) on two representative phonolitic compositions from Erebus (Antartica) and Teide (Spain) volcanoes. The laboratory spectra allow to determine precisely spectral emissivity in the thermal infrared (TIR), middle infrared (MIR), and shortwave infrared (SWIR) ranges. The results reveal the complexity and contrasted behavior of the radiative properties of the two rocks melts, despite their broadly similar composition. The spectral emissivity varies significantly as a function of temperature, composition, crystallinity, thickness, and thermal history. Altogether, the data reveal that emissivity cannot be considered as a constant value and question previous arguments that active lava always has lower emissivity than frozen lava. Finally, the laboratory-measured values of spectral emissivity were used to refine the temperature of Erebus lava lake gathered from previous remote-sensing methods.

Applications of Remote Sensing and Near-Surface Geophysical Methods for the Investigations of a Morainic Ridge Susceptible to Landslide. A Case Study from the High Lands of the Himalayas, Pakistan

Applications of Remote Sensing and Near-Surface Geophysical Methods for the Investigations of a Morainic Ridge Susceptible to Landslide. A Case Study from the High Lands of the Himalayas, Pakistan

Detecting and Classifying Rooftops with a Cnn-Based Remote Sensing Method for Urban Area Cool Roof Application

Detecting and Classifying Rooftops with a Cnn-Based Remote Sensing Method for Urban Area Cool Roof Application

An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021

In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.

Global Harmonization of Urbanization Measures: Proceed with Care.

By 2050, two-thirds of the world's population is expected to be living in cities and towns, a marked increase from today's level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the "statistical urbanization" concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban-rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today's urban policies are often focused.

Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.

Condition Assessment of Subsurface Drained Areas and Investigation of their Operational Efficiency by Field Inspection and Remote Sensing Methods

Abstract The extreme weather events highlight the need to develop action concepts to maintain agricultural production security in the future. Hydrological extremes can occur within a year in the form of surplus water (i.e. inland excess water), water scarcity or even drought. These adverse effects are influenced, inhibited and also facilitated by human activity. Previously, complex amelioration interventions, including subsurface drainage, aimed to improve the productivity of agricultural areas with unfavourable water management properties. The current efficiency of the subsurface drain networks in the regulation of groundwater level or soil moisture content can be questioned from several aspects. After the end of the socialist era (after 1990s), lack of maintenance and operation tasks have become typical, and are still a problem today in Hungary. Unfortunately, there is no exact national cadastre on the tile drained areas, and data is only available to a limited extent in the original amelioration plan documentations. In the present study, we aimed to reveal the possibilities of delineating the subsurface drained areas, and to develop a new method of condition assessment. Three tile drained study sites were selected on the Great Hungarian Plain in Central Europe. Our field investigations revealed the typical problems of the drained areas: (1) excessive vegetation of the receiving channels; (2) inadequate condition of the receiving main channel bed; (3) soil compaction in multiple layers above the drainage network; and (4) poor condition of outlets of the drain pipes. The developed methodology enabled us to evaluate the soil and the surface/subsurface water of the tile drained areas, and the technical condition of the drains. The necessary action plans or treatments were also outlined to replace the unused drain networks into use. Based on the scientific literature, we also sketched the target conditions and technological solutions that are required for the installation of new drains. The organization of the derived data into a GIS database could serve as a basis for the development of a cadastre of the tile drained areas based on a regional approach.

Spatial expansion and patterns of land use/land cover changes around Accra, Ghana – Emerging insights from Awutu Senya East Municipal Area

For most cities in the developing world, rapid and uncontrolled expansion at their fringes is a regular feature. In many cases these rapidly growing peri-urban areas are carved out as new local government units to function separately from the expanding city. Hence, the hitherto agricultural areas are transformed into residential and other urban uses. The overall result of such dynamic spatial and socio-political processes is the rapid change in land use/land cover (LULC). In this study, we assess, measure and explain LULC changes in Accra’s western peri-urban areas with the Awutu Senya East Municipal area (ASEMA) as the spatial context. Located on the west boundary of the Greater Accra Metropolitan Area (GAMA), the ASEMA is affected by the rapid expansion of GAMA. Adopting GIS and Remote Sensing methods, the study analyses the rate of urban expansion and LULC changes spanning 2014–2020. The study revealed that the urbanised areas have more than doubled representing a 152% change in the urban expanse over the six-year period under investigation. While this could be attributed to the rapid growth and spill-over from GAMA, the study also reveals socio-political and economic factors contributing to rapid changes in LULC in ASEMA. Specific boundary management strategies have been proposed to control the rapid expansion around the Municipality and also help address socio-spatial conflicts over boundaries.

The discovery of structural elements and zones of hydrothermal alterations by using ASTER satellite data in the margins of Gadabay and Murovdag ore districts (Lesser Caucasus, Azerbaijan)

&#x0D; &#x0D; &#x0D; The article considers the discovery of structural elements, zones of hydrothermal alterations and mineralization in the margins of Gadabay and Murovdag regions and the study of prospective areas for mineralization characterized by them by using ASTER satellite data. The main purpose of the research is the definition of the lithological composition of rocks, ring and linear structures and zones of hydrothermal alterations with the help of remote-sensing methods and by better technological development of special radiometric data. In order to detect the lithological composition of associated rocks of alteration zone of endogenous ores, 14 channel data of ASTER has been used. By using stereoscopic images obtained from ASTER data, it was determined that hydrothermal alterations are mainly represented by alunitization, kaolinization, sericitization, silication, pyropilitization and silification in the studied ore regions. These changes were shown to be characteristic for copper-porphyry, gold- copper pyrite, copper-polymetal and gold-copper-porphyry deposits. Strip ratios, principal component analysis (PCA), minimum of noise fraction (MNF) and selection method for least squares (LS) have been used to map hydrothermal alteration zones. The ring structures detected in the area, different fault fractures and lineaments are assessed as favourable geological structural factors for alteration and mineralization. The results obtained by ASTER image analysis confirm the presence of derivative minerals which are considered to be the product of hydrothermal alterations which are densely concentrated (kalium spathization, kaolinization, serictization, pyrophyllitization, alunitization) has been confirmed. The points each with private coordinates have been determined (defined) within separate areas with the help of remote data and relevant anomalous maps prepared. These points are considered favourable geological condition in terms of the type and intensity of hydrothermal alterations in the area. Such areas are considered a sign of indirect search significant for the detection of copper-polymetallic, copper-porphyry, copper-pyrite and gold-pyrite type of mineralization with epithermal origin in the region.&#x0D; &#x0D; &#x0D;