Sentinel-3 Research Articles

Quantifying methane emissions in major rice growing areas of Tamil Nadu using remote sensing and land surface temperature model

Rice (Oryza sativa L.) is cultivated in diverse environments, contributing significantly to global methane (CH4) emissions, accounting for approximately 12% of total methane emissions worldwide. With the demand for increasing rice production, methane emissions from rice fields continue to rise. This underscores the need for reliable strategies for estimation and mitigation strategies. This study aims to estimate methane emissions from rice fields of the Cauvery Delta region of Tamil Nadu using remote sensing data. Sentinel 1A Synthetic Aperture Radar (SAR) data was used to delineate rice areas and assess agronomic flooding, while Land Surface Temperature (LST) derived from MODIS satellite data was used to estimate methane emissions. The semi-empirical methane emission model was employed to estimate methane flux based on temperature-related factors and rice area. The spatial methane estimates derived from the LST-based method were compared with field observations. The results showed that during the Kharif and Rabi seasons of 2023, a total of 169679 and 356270 ha of rice area were delineated, respectively. The total methane emissions of 7.16 and 17.09 Gg were estimated for both seasons, respectively. The agreement between estimated and observed methane for both seasons was 84.74 % and 87.52 %, respectively. This study provides an efficient empirical method for estimating methane emissions across large areas and highlights the need for continued monitoring and the development of mitigation strategies to reduce methane emissions from rice cultivation.

Remote sensing analysis and geodynamic setting of magmatic spessartine-almandine-bearing leucogranites, Um Addebaa area, southeastern Desert, Egypt: Bulk rock and mineral chemistry

This study aims to incorporate optical multispectral remote sensing, petrography, geochemical, and field investigations in producing mapping and surface abundance of garnet mineralization within Um Addebaa area, southeastern Desert of Egypt. Datasets of Landsat-8 OLI/TIRS, ASTER and Sentinel-2A (S2A) were handled and scaled enhanced to discriminate the units of lithologic rock and garnet mineralization. The spectral mapping techniques of constrained energy minimization (CEM) and matched filtering (MF) are used to detect the garnet-rich zones which confined to the leucogranites masses. These granites are spessartine-almandine-rich, certainly along the interaction with the ophiolitic mélange. Notably, they have high total alkalis, SiO2, and Al2O3, Rb, 104Ga/Al, Y, Zr, Pb, Rb/Sr, Al2O3/TiO2, and FeO/MgO, and strong Ti, Sr, and Ba depletion as well as low CaO/Na2O ratio (<0.3). These geochemical parameters reflecting calc-alkaline, and strongly peraluminous magma of S-type. They are derived by two episodes of partial melting clay-rich metapelite crustal rocks followed by extreme Fe-Ti oxides and feldspar fractionation. These leucogranites formed within extension tectonic regime during post-collisional episode, resulting raising of asthenosphere and partial melting of the crustal metasedimentary rocks. Garnet occurs as, aggregates and/or vein like-shape, subhedral to rounded grains, homogenous and rarely reveal weak zonation. It’s cracked and free of inclusions. It contains appreciated concentrations of FeO and MnO and minor amounts of CaO and MgO, dominantly spessartine-almandine end-member (Alm52-59Sps 31-36 Grs 8-12 Prp0.5-0.8 Adr0). Garnet chemical composition reflect it is magmatic origin with MnO- FeO-rich magma that is produced from in situ nucleation from strongly peraluminous magmas during post- collision setting.

DETERMINATION OF SOLAR REFLECTION COEFFICIENTS (ALBEDO) FROM SATELLITE IMAGES USING GOOGLE EARTH ENGINE PLATFORM

In many models calculating solar radiation, a combination of physical measurements and mathematical models is used to achieve results close to reality. In these calculations, the slope values and shading effects in the region being analyzed are often disregarded. Mathematical models such as ArcGIS's Area Solar Radiation (ASR) can calculate shading effects on three-dimensional surfaces. When solar radiation models are computed in three dimensions, accounting for solar rays reflected from the ground, in addition to atmospheric reflections, will increase accuracy. This study aimed to determine the surface reflectance coefficients that should be added in three-dimensional radiation models. In literature, general assumptions exist for surface reflectance coefficients, which represent very broad average values. However, this study aimed to establish precise albedo values for all land classes and surfaces. An area of approximately 1600 km² located in the mountainous region south of Karaman was chosen as the test area. This area was chosen in Karaman province because, as is known, this region has high solar energy potential. Sentinel 2A satellite images with a spatial resolution of 10 meters were used for both summer and winter seasons through the Google Earth Engine (GEE) platform. For the summer and winter applications, the albedo value for snowy surfaces was calculated as 0.86, while for light-colored buildings, it was 0.36 for summer and 0.28 for winter. Although examples were provided for some land classes, the study ultimately determined albedo values for all land surfaces without differentiation between classes.

Features of Empirical Bio-Optical Algorithms for Estimating Chlorophyll-a Concen tration from Satellite Ocean Color Data in Waters around the Antarctic Peninsula

The features of the empirical bio-optical algorithm operation in the waters around the Antarctic Peninsula are analyzed based on a comparison of calibrated data from the shipborne flow fluorimeter and satellite data from the OLCI radiometer on Senti nel-3A and Sentinel-3B satellites during the Antarctic summers of January-February 2020 and 2022. It is shown that the standard OC4 bio-optical algorithm significantly underestimates satellite estimates of Chl-a concentration from ~1.5 to ~9 times (on aver age by a factor of ~3.1). The known regional OC4-SO algorithm provides acceptable errors of Chl-a concentration estimates and can be used for studies related to the analysis of Chl-a concentration in the waters around the Antarctic Peninsula. The developed in this work new regional algorithm OC4-AP has significantly lower error in comparison with the known standard and regional algorithms. It can be used if it is necessary to obtain a remote estimate of the concentration of Chl-a, as close as possible to the accumulated world experience in determining this value by standard extract spectrophotometric and fluorimetric methods. The observed underestimation of satellite estimates of Chl-a concentration using the standard empirical bio-optical OC4 algorithm can be attributed to at least three reasons typical for the studied water area: low relative CDOM content, high phycoerythrin content, and stronger effect of pigment packing in phytoplankton cells compared to the average values in the World Ocean.

Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed

Indonesia controls 75%-80% of the world's carbon stocks, so the amount of carbon stocks must be utilized optimally. This study aims to determine carbon stocks, potential emissions, and economic value of carbon stocks in each land use. The method used is secondary data analysis and field checking. The data collected were Sentinel 2A acquisitions in 2020 and 2022, Digital Elevation Model (DEM), and land use land cover in 2020-2023. Data analysis used SNAP and ArcGIS 10.8. The tool used for data analysis is spatial analysis map algebra. The results showed mixed dryland agriculture has the most extensive carbon stock, at 2,614,178 tons/ha, with potential emissions of 9,585,320 tons/ha. The most minor carbon stock is in mining land use, which is 0 tons/ha with potential emissions of 0 tons/ha. The highest C02 value in USD is the forest land use group. In the Secondary Dryland Forest, Secondary Swamp Forest, and Plantation Forest groups, it is 17,517,400.50 USD, while the lowest is mining land use, which is 0 USD. Overall, the CO2 value of land use in the study area is 34,246,314.45 USD. Integrating remote sensing data analysis and field surveys in geospatial technology is one of the new approaches to studying carbon stocks and CO2 emissions in topsoil from various land uses. By utilizing geospatial technology, efforts to estimate carbon stocks on the surface are easier and faster.

Unveiling subsidence patterns: Time series analysis for land deformation investigation in the west-Qurna oil field, Iraq

Land subsidence is a worldwide geological and environmental risk caused by natural occurrences and human actions. Its effects include a range of socio-economic, environmental, and hydrogeological consequences, such as damage to infrastructure like buildings, roads, bridges, and pipelines, as well as increased flooding and reduced groundwater storage capacity. Due to these diverse impacts, it is crucial to monitor the spatial and temporal scope of land subsidence. This study presents an investigation into land subsidence within the West-Qurna oil field, a large oil reservoir situated in Iraq's Basrah governorate. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) analysis from the European Space Agency Sentinel 1A over six years, from June 2017 to May 2023. The Stanford Method for Persistent Scatterers (StaMPS) has been utilised to assess the scale and magnitude of land deformations in this region. Results revealed a notable subsidence within the central urban area of the oil field, forming an ellipsoidal subsidence bowl spanning 86 km2. The peak subsidence rate is identified at −13.2 ± 0.4 mm/yr within this bowl, with a cumulative vertical displacement of 75 mm throughout the six-year observation period. Furthermore, uplifting phenomena are also detected at the study area's peripheries, reaching a maximum rate of 12 ± 0.4 mm/yr and a cumulative shift of 54 mm. Temporal analysis showcases a significant alteration in subsidence rates, with rates of −18 mm/yr observed between 2017 and 2020, followed by −5 mm/yr post-2020. This change is attributed to COVID-19-related oil production reductions enacted by the government to boost prices. Our analysis points toward oil extraction as a probable primary driver of subsidence in the studied area, although a deeper probe into the impact of groundwater extraction for reservoir injection remains essential.

A framework for generating rating curves in Mahanadi River using hydrodynamic model and radar altimetry data

ABSTRACT The limited availability of in-situ data has drawn attention towards using remote sensing techniques to monitor river flow. Radar altimetry data has been used to generate stage-discharge rating curves through power-law relations and empirical methods. However, evaluating hydrodynamic models for rating curve generation using multi-mission altimetry data in data-scarce regions is lacking. We used altimetry data (Jason 2, Jason 3, SARAL/AltiKa, Sentinel 3A, and Sentinel 3B) over the Mahanadi River to evaluate rating curves at virtual stations. The HEC-RAS hydrodynamic model was set up, identifying seven virtual stations along the Mahanadi River from Boudh to Mundali Barrage. Statistical evaluation of ‘water level’ (RMSE 0.27-0.88 m) and ‘discharge’ (KGE 0.52 to 0.88) components of the generated rating curves showed significant agreement with radar altimetry data. These rating curves at virtual stations offer a cost-effective tool for monitoring river flows at additional locations.

Remote sensing of illegal dumps through supervised classification of satellite images: application in Oaxaca, Mexico

Various economic, social, and cultural factors have contributed to the proliferation of illegal dumps, causing urban image degradation, population health impacts, and soil, air, and water contamination. Scientists developed remote sensing techniques to identify these red spots and thus contribute to their mitigation and control. They recently used these techniques to detect large areas of illegal waste dumping instead of using expensive field monitoring. Artificial intelligence algorithms have been used to process satellite images due to the availability of satellite images and the increase in the processing capacity of computer systems. This work presents the results of a satellite remote-sensing procedure to detect illegal dumps in one hydrographic subbasin in Oaxaca, Mexico, through a supervised land cover classification using a Random Forest classifier. Two hundred and fifty-six control polygons were used to train the classifier. The classification criteria were the twelve bands of the Sentinel 2A satellite images with a spatial resolution of 10x10 meters, the spectral indices NDVI, MNDWI, SAVI, NDBI, BSI, and the surface slope. Google Earth Engine platform was used to process satellite images. There were 288,100 hectares classified in this way: 65.4% classified as vegetation, 31.5% like bare soil, 2.7% was urban soil and the rest was classified as water or garbage. A confusion matrix calculated the accuracy of the model in 0.9517. The model was not able to accurately distinguish between urban soil, bare soil and garbage due to the similarity of their spectral fingerprints. NDVI and SAVI were the most important spectral indices for detecting litter, and those might contribute to building a spectral fingerprint of litter in the future. Poorly classified areas were discarded through photointerpretation work and post-processing. Finally, thirty-two probable illegal dumps were identified, twelve of which were confirmed on the territory.

A Complete Study of Remote Sensing- Sentinel-2 Satellite Data for Land Use / Land Cover (LULC) Analysis

Remote sensing based Satellite collected Images is a complete advanced process of both the automatic detecting observation and overall earth monitoring as well as observing the mainly physical level of characteristics of an area by mainly measuring its reflected and emitted radiation at a distance mainly used from both satellite and aircraft. Special quality cameras collect remotely based sensed images, which help all researchers "sense" things about overall Earth observation. The European Research Space Agency (ESA) and the European (member countries) Union-EU both have equally provided together to specially a very powerful observation of the whole planet surface by simply making the remote sensing based Sentinel type-2 numerous spectral based results. The Sentinel category 2 satellite was successfully launched on June, 23rd 2015 to deliver satellite collection of data and imagery. The sentinel category-1 is the mission of the introductory satellite of Copernicus oriented research project for the artificial satellite based asterism organized by all Europe countries Space Research Agency. The satellite series of Sentinel 1A was successfully produced in the month of April, 03 2014, the same as Sentinel-1B satellite was successfully launched in the month of April, 25 2016. The Sentinel category-2 is the second category of satellite constellation of the All European countries Space Research Program designed mainly for a series of Sentinel missions. The main goal of the Sentinel category-2 satellite series mission is primary provide a very good quality of best pixel-resolution space satellite data / information used mainly for Land surface Cover (LC)/ Land Usage (LU) observation, coastal surface observation, different climate (Cloud) change and natural disaster damage mapping, Natural disaster, Earth Observatory, Forestry, Wildfire Assessment, Humanitarian operations with a best complementing to the other remote sensing satellite series missions mainly as-”LANDSAT”. Therefore, the review of advanced Sentinel type -2 is primary highlighted on these two important parameters: First, The main advantage of the ESA-Sentinel type 2 to different Land surface cover / Land usage classification or monitoring, second, Discovering total overall results of the ESA- Sentinel type-2 satellite data / imagery in various remote sensing based applications (such as the green-forest monitoring, urban area locality, natural level incident hazard analyze monitoring, coastal monitoring). The complete overall accuracy of data analysis can be improved when the possibility to simply integrate Sentinel type-2 satellite data with additional various types of advanced remotely based satellite observed details. The overall literature review of the Sentinel type-2 also describes a use of advanced remote sensing Sentinel type-2 details which are produced a very high level accuracies (&gt;81 to 85% by using various advanced machine-learning classification algorithms mainly as: Logistic standard Regression, Support based-Vector (classification) Machine-SVM, Decision level of Trees and Random standard forest-RF algorithm. Remaining, other classifier techniques commonly known as Maximum-Likelihood Analysis (MLA) are most commonly used. Although the sentinel type-2 also provides good level of opportunities mainly for Land surface cover-LC / Land usage-LU based on various classification, as well as they have many complex difficulties which added different mismatching with data of Landsat satellite based on OLI-category 8 additionally a gap of various thermal level bands, and different spatial based resolution (pixel) among the various bands of Sentinel type-2 satellite. The Sentinel-2 data has the good quality of probable to significantly present in the direction of land surface coverage with land usage monitoring as well as classification.

Mapping the water cover of a protected area in the Amazon using Sentinel 1A data

Mapping the water cover of a protected area in the Amazon using Sentinel 1A data

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

A Copernicus-based evapotranspiration dataset at 100 m spatial resolution over four Mediterranean basins

Abstract. Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a, https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3, https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9, and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d).

Revealing the Feedback Loop: Comparing Satellite and Sensor-derived Meteorological Parameters in Groundnut Cultivation

Background: Studying soil moisture’s impact on groundnut, particularly under rainfed conditions, is crucial for improving quality and yield. This research explores soil moisture dynamics using satellite and in-situ sensor data to enhance water management and crop predictions, focusing on the feedback mechanisms between soil moisture and weather variables. Methods: The study was conducted at the Oil Seed Research Station (TNAU) in Tindivanam, Tamil Nadu, using the groundnut variety TMV 14. Soil moisture and weather parameters were monitored at critical crop growth stages, using an in-situ soil moisture sensor (Cr 300) and compared with satellite data from SMAP, ERA5 and Sentinel 1A with environmental factors. Statistical analysis was carried out using SAS JMP Pro and network analysis via Cytoscape 3.8.2 software. Result: Pre-monsoon soil moisture SMAP, SMAP-RZSM and sensor (VWC at 10cm) were found to increase in post-monsoon. Strong positive correlations were attained between SMAP-SM and SMAP-RZSM (r=0.94), indicating a reinforcing loop. Whereas, the negative feedback was observed between SMAP-SM and deeper soil temperatures (-0.91). It underscores the dynamic interplay between soil moisture and atmospheric conditions, essential for developing efficient water management strategies and enhancing crop resilience to climate variability.

3D Surface Velocity Field Inferred from SAR Interferometry: Cerro Prieto Step-Over, Mexico, Case Study

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production.

Spatio-Temporal Analysis of Water Hyacinth Density (Eichhornia crassipes) in Lake Rawa Pening 2019 - 2023 Using NDVI Algorithm on Google Earth Engine (GEE)

Abstract Water hyacinth (Eichhornia crassipes) is an aquatic plant that floats in freshwater waters that has the ability to grow very fast and is invasive. This plant is able to reproduce vegetatively and generatively and is able to double its population within two weeks so that it can endanger the ecosystem in the waters. The purpose of this research is to map the distribution pattern, dynamics of changes in vegetation density and changes in water hyacinth area with spatial-temporal techniques using remote sensing technology processed using GEE in Lake Rawa Pening. The satellite image used is Sentinel level-2A with high spatial resolution with a recording time of May 2019 - 2023. This study uses the NDVI method to measure the greenness of vegetation and help identify the density of water hyacinth vegetation. The results of the vegetation density index show that there are five classification for vegetation density levels, and the outcomes of the accuracy test that was conducted range from -0.15 to 0.94. obtained an overall accuracy of 86.6% and a kappa coefficient value of 0.81. These results show that the use of Sentinel 2A imagery can be used for vegetation density index mapping using the NDVI algorithm in GEE with a good level of accuracy and conformity between map and field and the image processing that has been done is appropriate.

Multi-spatial Resolution Imagery to Estimate Above-Ground Carbon Stocks in Mangrove Forests

Mangroves are a type of vegetation that can absorb carbon and have an essential role in controlling CO2 levels in the atmosphere. Mangroves can absorb carbon better than terrestrial ecosystems because of their ability to bury carbon in sediment. This research aims to compare and measure the carbon stock content above the surface of mangroves in the field using multi-spatial resolution imagery, namely, Landsat 8 OLI, Sentinel 2A, and Planetscope. Field carbon calculations were carried out using the allometric method based on mangrove species. The calculation results are then linked through regression analysis with the vegetation index Difference Vegetation Index (DVI) results. The total carbon obtained from PlanetScope imagery was 535.27 tons, Sentinel 2A imagery was 549.23 tons, and Landsat 8 OLI imagery was 533.57 tons. Among the three images used, based on Sentinel 2A statistical analysis reflects the possibility of overfitting or the best with higher r and R2 values in the calculations. However, based on SE accuracy tests, PlanetScope has better accuracy than the other two images. Apart from that, the accuracy test results using a 1:1 goodness of fit plot for each image, the distribution pattern of mangrove carbon stock estimates shows that the entire model in mapping mangrove carbon stocks is over-estimated. The overestimated results are possible because more objects around the mangrove, especially canopy density, are recorded by remote sensing sensors compared to tree diameter as input for field carbon results.

A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images

ABSTRACT Cities are facing many challenges related to urban growth. This phenomenon has prompted decision-makers to adopt innovative approaches for planning based on accurate forecasting of urban growth. Among the most widely used forecasting methods, there are Cellular Automata (CA) based methods and Recurrent Neural Networks (RNN) based methods. The accuracy of these forecasting models is strongly related to data quality, data availability, Model calibration and Model validation. In this paper, a comparative analysis between three forecasting methods is presented based on a temporal sequence of Sentinel 2A images. The main goal of this study is to assess the performance of these models which are of CA-Markov Chain, MLP-Markov and ConvLSTM in terms of accuracy, complexity, and feasibility. The case study is carried out on the city of Casablanca in Morocco. After implementing these three forecasting methods, the obtained results show that the Kappa coefficient of MLP-Markov, CA-Markov and ConvLSTM is, respectively, 89,40%; 97,20%; and 94,50%. In terms of complexity, the ConvLSTM method is more complex due to the number of elementary operations. In terms of feasibility, the ConvLSTM method is more demanding in terms of data volume since it is a Deep Learning model. Accordingly, CA-Markov based methods, in particular MLP-Markov, show a great potential for forecasting urban growth, especially for short term forecasting when there are not enough satellite images available to adopt a Deep Learning approach such as ConvLSTM.

Monitoring Mining Impact for Geosites Using Time Series NDVI and Run-off in the Eastern Part of Southern Java Mountains, Indonesia

Several geosites in the Eastern Part of the Southern Mountains of Java which are currently being mined are important geosites because they have recorded tectonic processes in the tertiary period. However, mining activities without environmental monitoring could be lead loss of rock outcrops and alteration of mineralscontained in the geosite. This study aims to monitor the vegetation index and run off in karst geosites and gold mined hills. We used Sentinel 2A Imagery on the Google Earth Engine (GEE) to get the vegetation index (NDVI) and run off values temporally. The Sentinel 2A data acquisition pro-cess in both 2018 and 2022 was carried out on the Google Earth Engine with specific steps consisting import data from cloud collection, cloud masking, and customing date acquistion. The results show that there has been a decrease in the vegetation index in the period 2018-2022 which is indicated by the decrease in the vegetation index value in the mined area. Another impact found is that there has been an increase in the run-off value in 2022 in both the Karst Puger Hills and the Tumpang Pitu Hills. The NDVI pattern on mined geosites has also changed significantly due to increasingly intense mining activities. The NDVI fluctuation pattern in the Karst Hills in 2018-2020 ranges from 0.41 to 0.74, while in 2021-2022 the fluctuation pattern is only in the range 0.05 to 0.4, respectively. The NDVI fluctuation pattern in the Tumpang Pitu Gold Hills in 2018-2019 is in the range 0.6 to 0.8, while in 2020-2022 it is in the range 0.38 to 0.52, respectively. Google Earth Engine is able to map the vegeta-tion index more efficiently by using a time series approach. These advancement is different compare to previous studies, where our study shows rapid acquisition during pre-processing, performing NDVI with rapid temporal analysis, and shows numerous degraded land in southern java mountains. There-fore, it can be concluded that there has been a decrease in the vegetation index and an increase in run off which can threaten the rock outcrop on the geosite. Furthermore, this study suggest that GEE should be considered as a main tool to identified degraded land, particularly for geosite conservation.

An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

Abstract The largest tropical peatland complex in the Cuvette Centrale, is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC). Utilizing peatland extent and Radar for Detecting Deforestation (RADD) alert data, we analyzed spatial and temporal patterns of disturbances. Further, we examined 2,267 randomly sampled peat forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district. Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of each year with temporal hotspots emerging between February and May, closely associated with smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances in Cuvette Centrale, representing a leading role both in ROC (~77%) and DRC (~89%). While, small-scale logging contributed 7% to disturbances in the region, it constituted an important driver (18 %) in the ROC. Other drivers included flood, roads and settlements. Approximately 77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40% extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat forest edges and ~76% of the disturbances occurred within 5 km of road or river networks. The insights underscore the crucial need for effective peat forest conservation strategies in Cuvette Centrale and can inform national policies targeting peatland protection, aligning with commitments in the Brazzaville Declaration and Paris Agreement. Further, our findings on direct driver assessment could serve as a reference dataset for machine learning models to automate the visual interpretation and upscale the assessment across the entire region.

Transit-oriented development (TOD) or development-oriented transit (DOT): how does it matter for sustainable urban growth?

ABSTRACT Urban growth patterns occur in various ways with tangible practical consequences. A key dilemma with regard to development and transportation revolves around whether the former follows the latter or vice versa. This study assessed this nexus with transit-oriented development (TOD) and development-oriented development (DOT) cases by using the land use/land cover (LU/LC) classification technique in a developing country example of a city in Turkey. Spatial data included high-resolution Sentinel 2A, Rapid Eye, and two SPOT satellite images and CORONA air photos acquired in 2021, 2013, 2000, 1989, and 1979, respectively, within three buffer zones (500, 750, and 1000 m) for both development forms. While the study acknowledges unorthodox TOD selection criteria and generalizability concerns, the results demonstrate that the TOD drastically reduced green areas (as opposed to ideal TOD requirements), while the DOT excessively grew commercial land uses and street networks. Both developments commonly impaired agricultural, vacant, and water land use. Such transformations direct local governments, transit agencies, and city planners to resolve ideal sustainable urban growth patterns by using remote sensing data and quantitative analysis.