As the demand for coal continues to rise due to the increasing need for energy, the number of open-cast coal mines has also increased. However, the environmental damage caused by open-cast mines has become a significant concern in today's world amidst escalating environmental problems. Surface mining involves the removal of vegetation and soil from the surface, followed by the blasting of subsoil to access the coal resource, resulting in a complete transformation of land use. In Türkiye, as in many other countries, open cast mines are generally situated outside the cities, often within forested areas.

Milas Ören Lignite Coal Mining Quarries, located in southwestern Turkey, is one of Türkiye's largest open cast coal mines. Since its opening in 1979, the total mining area has expanded to encompass 2.023 hectares by 2022, leading to the gradual elimination of surrounding forest, maquis, and agricultural lands. In this context, this study aims to assess the impact of open-cast lignite coal mining on the vegetation surrounding the Milas-Ören mining site.

Change detection analysis was conducted in Google Earth Engine using Landsat 5-8 satellite imagery obtained between 1985 and 2023 and to achieve this objective. The analysis, performed at 5-year intervals (1985-1990-1995-2000-2005-2010-2015-2015-2020-2023), revealed a loss of approximately 725 hectares of forests and 440 hectares of agricultural land, while the mining areas expanded by over 1.100 hectares. Consequently, this study demonstrates that the impact of mining activities on vegetated areas can be effectively measured and monitored using remote sensing tools, particularly in the context of the increasing global attention towards deforestation and the development of deforestation mitigation strategies.

Overall, this research sheds light on the temporal changes occurring in vegetated regions surrounding open cast quarries, specifically focusing on the Milas-Ören Lignite Coal Quarries in Türkiye. The findings contribute to the growing body of knowledge on the environmental impacts of mining activities and highlight the potential of remote sensing techniques for monitoring and mitigating deforestation in similar contexts.

This study focuses on evaluating the unique capabilities of ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses resulting from the selective logging process within a sustainable framework in the Tapajós National Forest (TNF), situated in the heart of the Brazilian Amazon. Specifically, two areas within TNF characterized by intensive logging activities, ranging between 27 m³ ha⁻¹ and 29 m³ ha⁻¹, were chosen as Annual Production Units (APUs). Each APU was logged during a distinct year: APU 2016 and APU 2017. Extracting attributes from ALOS2 images, encompassing backscatter properties (including algebraic calculations, band ratios, SAR vegetation indices, and texture measurements) and phase information (comprising entropy and alpha angle), this investigation aims to detect forest volume losses. This involves evaluating the disparities in pixel values between logged and unlogged regions. The analysis employs Wilcoxon's nonparametric test at a 95% confidence level to determine the statistical significance of the observed differences. The findings gleaned from ALOS2 data demonstrate robust performance. Among the considered attributes, the Radar Normalized Difference Vegetation Index (RNDVI) emerges as the most promising indicator for detecting forest volume losses attributed to degradation through selective logging. Notably, this effectiveness is consistent across both investigated areas, with a p-value of 0.003 for APU 2016 and 0.037 for APU 2017. Additionally, the cross-polarization ratio and the texture measure known as Contrast in HV polarization display significant potential. This study underscores ALOS2's efficacy in identifying forest volume losses arising from selective logging. The insights gained, particularly the prominence of RNDVI in degradation detection, offer valuable perspectives for monitoring and mitigating ecological impacts stemming from logging activities within intricate forest ecosystems.

Climate change has directly impacted Earth's habitats, resulting in various adverse effects, such as the desiccation of water bodies. The process of identifying such changes through field observations is time-consuming and costly. By using remote sensing techniques, it has become easier than ever to monitor changes in the environment. Radar satellites, unlike optics, can acquire data in all weather conditions and regardless of the time of day. These data can provide valuable information about the environment and surface roughness. Various methods have been proposed for detecting changes, which can be divided into classic and deep learning methods. Classic methods only use image information such as radar backscatter and cannot extract spectral-spatial information. Sentinel-1 (S1) is an Earth observation radar sensor that provides free access to SAR (Synthetic Aperture Radar) images. This study aims to survey the performance of two classic methods ratio index, Markov Random Field (MRF) with deep learning networks in detecting changes. As a deep network, Inception CNN (convolutional neural network) is presented as an enhancement of CNN to detect the changes. To evaluate methods, two times of S1 images from Lake Poopó, located in the Altiplano Mountains in Oruro Department, Bolivia, are used as a primary dataset. The results of the comparison models were assessed using three evaluation metrics: Overall Accuracy (O.A), Missed Error (M.E), and Kappa Coefficient (K). Based on the evaluations, the Multi-scale CNN performed exceptionally in all metrics, with O.A, K, and M.E rates of 97.35%, 90.28%, and 9%, respectively. Meanwhile, the ratio index had poor performance, with 83.27%, 29.05%, and 75.03%, respectively, for O.A, K, and M.E. These results indicated that the deep networks could provide better performance in detecting changes from S1 images.

Digital Elevation Model (DEM) is a 3-dimensional digital representation of the terrain or the Earth’s surface. It is the ideal and most widely used method for determining topography with (i.e. Digital Surface Model) or without the objects (i.e. Digital Terrain Model). DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and Radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy except for the VHR datasets acquired from UAVs having spatial resolution of few centimeters. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. Machine learning regression algorithms are implemented in this study to estimate a DEM from ICESat-2 LiDAR point data using CartoDEM V3 R1. This study was conducted over a hilly terrain of Dehradun region in the foothills of Himalayas in India. The robustness of these algorithms has been tested for a plain region of Ghaziabad, Uttar Pradesh, India in an earlier study. Various regression-based machine-learning techniques were compared to interpolate DEM from ICESat-2 data. The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron (MLP) Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested. The accuracy of the simulated ICESat-2 DEM using MLP Regressor was assessed using the DGPS points collected over the area and the RMSE was of the order of 6.58m.

Land surface temperature (LST) plays a crucial role in the dynamic energy interchange between the land surface and the atmosphere. Utilizing thermal infrared (TIR) remote sensing constitutes a significant method for effectively capturing LST across extensive geographic regions. Over the course of several decades, researchers have dedicated their efforts to refining algorithms for deriving LST from TIR remote sensing data. Among these algorithms, the split-window (SW) technique stands out, as it directly mitigates atmospheric distortions by leveraging the brightness temperature (BT) from two adjacent TIR channels at the top of the atmosphere.

The Joint Polar Satellite System, JPSS-2/NOAA-21, represents the most recent launch in September 2021 by the National Oceanic and Atmospheric Administration (NOAA). Its primary objective is to furnish comprehensive global environmental data, encompassing insights into weather patterns, atmospheric dynamics, and various environmental indicators. Achieving this mission involves a constellation of polar-orbiting satellites. Remarkably, JPSS-2/NOAA-21 delivers two-channel Thermal Infrared (TIR) imagery, characterized by a specific spatial resolution. Consequently, the advancement of the Split-Window (SW) algorithm for Land Surface Temperature (LST) retrieval becomes especially pertinent in this context. In this study, SW algorithm was developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global NOAA LST products were selected to evaluate the accuracy of the proposed algorithm. Validation and comparison with ground-based measurements or existing LST products showcase the algorithm's efficacy in providing accurate and reliable land surface temperature estimates over diverse landscapes and climatic conditions. The results show that the ground validation accuracy is about 1.4 K, demonstrating the potential of the split-window algorithm to contribute significantly to land surface temperature monitoring, climate studies, and environmental management initiatives utilizing data from the JPSS-2/NOAA-21 satellite system.

Radiative Transfer Models (RTMs) are one of the major building blocks of remote sensing data analysis that are widely used for various tasks, such as atmospheric correction of satellite imagery. Although high-fidelity physical RTMs like MODTRAN offer the best possible modeling of atmospheric procedures, they are computationally demanding and require many hyperparameters that are difficult to set by a nonprofessional user. Therefore, there is a need for surrogate models for the physical radiative transfer codes that can mitigate these drawbacks while offering an acceptable performance. This study aimed to suggest surrogate models for the MODTRAN RTM using machine learning and deep learning algorithms. For this purpose, the top-of-atmosphere (TOP) spectra were calculated by the MODTRAN code, and the bottom-of-atmosphere (BOA) input spectra and other atmospheric parameters like temperature and water vapor content observations were collected for the training dataset. Three deep learning regression models, including a fully connected network (FCN), a 1-D convolutional neural network (CNN), and an auto-encoder (AE), as well as the random forest (RF) machine learning regression model, were trained using the collected dataset. The results of these models were assessed using three evaluation metrics of root mean squared error (RMSE), regression coefficient (R²), and spectral angle (SAM). The evaluations indicated that the AE offered the best performance in all the metrics with RMSE, R², and SAM scores of 0.0047, 0.9906, and 1.3987 (degrees), respectively, in the best-case scenarios. Moreover, the random forest model performed worst with RMSE, R², and SAM scores of 0.0077, 0.9507, and 2.1443 (degrees) in the best-case scenarios. These results proved the highly non-linear nature of the radiative transfer codes and showed that the deep learning models could better model the high-fidelity physical RTMs.

Water plays a vital role in sustaining life and meeting the water needs of various sectors such as agriculture, industries, and households. As water resources continue to be depleted, several hazards arise for the communities. Declining water quality, a declining water table, reduced plant growth, droughts, diminished agricultural productivity, and the underutilization of power generation stations are some of the hazards associated with these conditions. Therefore, it is crucial to monitor the changes in water resources. The traditional methods used for measuring water storage face various challenges, including limited spatial coverage, low temporal resolution, high cost and resource requirements, and accuracy limitations. To address this challenge, remote sensing sensors such as the GRACE satellite provide a rich source of information that can be used to evaluate water reserves. Moreover, the Google Earth Engine provides access to a wide range of satellite imagery and geospatial data for various applications, making geospatial information more accessible and enabling informed decisions. This study analyzed the GRACE satellite time series data from 2002 to 2017 to investigate and compare water storage changes in Iran and six neighboring countries: Turkey, Iraq, Saudi Arabia, Turkmenistan, Pakistan, and Afghanistan. The final statistical analyses indicate a decrease in water reserves in almost all mentioned countries. The analysis of the results shows that Iran ranks second in terms of water reserve consumption after Iraq, which had the worst performance. Our study concludes with a concerning outlook on water storage in Iran, primarily attributed to inefficient water resource management, reduced rainfall, drought, and excessive withdrawals.

Solar radiation reflected by the Earth´s surface to satellite sensors is modified by its interaction with the atmosphere. The application of atmospheric correction of optical satellite imagery is an essential and needed pre-processing tool for modeling biophysical variables, multi-temporal analysis, and digital classification processes. As a result, true surface reflectance values are obtained without atmosphere influence.

To assess this process, GEOSAT (part of the ESA’s Third-Party Mission Programme) performs an optimization of the GEOSAT 2 very high resolution (VHR) multispectral imagery adapting the well-known 6S model (Second Simulation of a Satellite Signal in the Solar Spectrum, Vermote, 1997) to the different wavelengths covered by the GEOSAT-2 spectral bands (VHR, PAN). 6S model predicts surface reflectance (BOA) using information from the apparent reflectance (TOA) captured by the satellite sensor and the corresponding atmospheric conditions.

To perform the atmospheric correction (AC), both the configuration of the atmosphere at the time of capture and the conditions of scene pointing and luminosity, must be considered. The first is mainly determined by three values: water vapor, ozone and the number of air-suspended particles (aerosols). For the latter, the geometry of the scene, as well as the respective sun and sensor observation positions are the values to be considered.

To validate the resultant GEOSAT-2 AC images, obtained from applying the GEOSAT atmospheric correction algorithm, different common areas between these and Sentinel-2 L2A products have been selected. Then, band-by-band (R, G, B & NIR) operations, such as calculation of the mean square error (RMSE) and a regression analysis were performed. Then, spectral profiles for the three generic land coverages (vegetation, soil and water) were also gathered over the spectral range of GEOSAT-2 and S2 corresponding bands. The outcomes, once analysed, lead us to conclude that the results obtained by applying the promising GEOSAT AC algorithm are satisfactory and seem to correctly estimate BOA reflectance values for vegetation and water coverages. To extend the study and improve the result ground reflectance values will be required.

In recent years, coastal erosion has become one of the many natural hazards affecting Chile's sandy coastlines. Currently, more than 90% of the sandy coasts of Valparaíso drop off from coastal erosion. Cartagena Bay, located in the municipality of San Antonio, is one of the coastal areas with the greatest transformations caused by extreme events and anthropogenic activities. These transformations require continuous monitoring, and medium-resolution optical satellite imagery is seen as an invaluable resource for tracking these coastal changes. In this study, a littoral analysis is presented that combines optical satellite imagery, simulation-derived wave climate, in situ data, the SHOREX system developed in Python, and GIS-based tools such as DSAS to quantify rates of change in the Bay over the period 1986-2022. Satellite-derived shorelines were used to identify erosion hotspot areas in the Bay, differentiating the impact of erosive processes associated with ENSO hydrometeorological phenomena, the 27-F 2010 earthquake and tidal waves from 2015-2022, which lead to major transformations in the morphodynamics of the beach. The results show that the Bay is currently undergoing high erosional processes in 20% of the coastline with values < -1.5 m/year and 50% with erosion rates ranging from [-0.2 to -1.5 m/year]. Since 2015, these processes have been accentuated, due to increased swells throughout the year. The results were validated with data taken in the field during the satellite revisit days and a pressure of 4.52 m was obtained, guaranteeing sub-pixel precision.

Monitoring photosynthetic (PV) and non-photosynthetic vegetation (NPV) fractions over time can help to detect and assess changes in ecosystem function and services which are critical for resource management and the mitigation of climate change effects. Mixed tree-grass ecosystems are one of the most prevalent forms of terrestrial vegetation across the globe. They provide the basis for livestock production and significantly impact regional and global productivity and food quality. However, these ecosystems represent a challenge for remote sensing applications due to the spectral heterogeneity stemming from the multiple landscape features such as grasses, trees, and shadows. Such a mixture makes it difficult to accurately identify and characterize the different vegetation layers at pixel level in most satellite imagery.

The aim of this study was to use Sentinel 2 (S2) images to estimate PV and NPV fractions in the herbaceous layer of a tree-grass ecosystem and develop empirical models to relate estimated NPV fractions and key vegetation traits associated to senesced vegetation such as Aboveground biomass (AGB). This information will allow to analyze the impact of NPV spatio-temporal variability on grassland productivity as standing senescent plants can compete with green vegetation and difficult seed germination [1].

Spectral mixture analysis (SMA) [2] is a powerful technique that can separate mixed spectral signals to estimate the fractional cover of individual landscape features. SMA and similar unmixing techniques are often applied without considering the spectral phenology of the vegetation layers which may reduce the accuracy and reliability of the results. In this work, we addressed these limitations by using a seasonal endmember spectral mixture analysis (SESMA) approach that accounts for temporal endmember variability. We downloaded and processed a S2 time series of 449 images from 2016 to 2022. Atmospherically corrected Surface Reflectance sub-scenes of about 1.1 x 1.3 km centered on the study area at the Majadas de Tiétar research station (https://www.bgc-jena.mpg.de/majadas) have been analyzed in combination with spectral and biophysical in-situ data acquired at the site to estimate PV and NPV grass fractions and predict AGB of the NPV vegetation. The SESMA S2 endmembers were derived from field spectra measured with an ASD Fieldspec 3 spectroradiometer (400 to 2500 nm) including PV, NPV and soil measurements acquired in different phenological periods. A partially constrained unmixing with a sum-to-unity constraint on the abundance fractions was applied using ENVI 5.7 image analysis software. Linear regression was further applied to resulting S2 image fractions to relate NPV fraction within-situ NPV AGB as derived from the senescent vegetation samples acquired in 5 to 12 permanent plots in 34 campaigns from 2017 to 2022. Best results (R² =0.46) were achieved during the grass decay season, where a mixture of green and senescent species is usually found in semi-arid grasslands. An important finding of our study was the effect of flowering, that caused the shifting from direct to inverse relationship between NPV fraction and NPV AGB, which confirms the need to consider phenology, including flowering, in the development of NPV estimation models in highly dynamic herbaceous covers.

[1] Xu, D., Liu, Y., Xu, W., & Guo, X. (2022). The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands. Remote Sensing, 14(13), 3031.

[2] Shimabukuro, Y. E., & Ponzoni, F. J. (2019). The Linear Spectral Mixture Model. In Spectral Mixture for Remote Sensing: Linear Model and Applications (pp. 23-41). Cham, Switzerland: Springer International Publishing.