Satellite Imagery Processing Research Articles

The Identification, Spatial Distribution, and Reconstruction Mode of Abandoned Mining Areas

The rehabilitation of abandoned mining sites is an increasingly pressing issue in the context of sustainable development. Recent research has emphasized the need for a holistic approach to the abandoned mining sites and their environmental rehabilitation. Based on field analysis, environmental assessments, satellite imagery processing and geographic information operations, this paper pushes forward the existing knowledge by conducting a comprehensive assessment of abandoned mining sites in the Romanian Carpathians and by proposing innovative and sustainable rehabilitation solutions. Our findings highlight that abandoned mining sites and their surrounding territories in the Romanian mountains have significant ecological imbalances and complex socio-economic issues. The findings also suggest that by adopting innovative, integrated, and sustainability-oriented approaches, territories affected by mining can be transformed into valuable and sustainable spaces to meet human needs. We conclude by presenting the importance of innovation in ecological reconstruction and spatial–functional reintegration of mining sites in mountain areas as a useful tool in making fair decisions, both in the context of implementing appropriate development policies as well as for the resilience and environmental sustainability of mining-affected mountain areas.

Progressive updates of convolutional neural networks for enhanced reliability in small satellite applications

Small satellites enable many important applications for both economic and scientific purposes. Many of these applications are inherently data-centric and rely on large amounts of high-resolution satellite imagery to be delivered in a timely manner. However, communicating this data to Earth is challenging due to intermittent connectivity, high packet losses, low data rates, and similar issues. Therefore, efficient onboard prioritization and data processing are essential for future satellite missions. Machine learning methods, such as deep neural networks, are very suitable for such prioritization, as they are already used extensively for satellite imagery processing and they can be deployed onboard of satellites. However, updating them to support new classification requirements when the satellite is already in orbit is difficult, as often multiple passes are required to complete model transmission due to the communication challenges. To cope with this issue, we propose a progressive transmission mechanism for model updates, which leverages vector quantization and arithmetic coding. Our mechanism allows to achieve high accuracies even with partially updated models. Evaluation results show that our mechanism significantly outperforms other less optimized transmission schemes.

UAV Mapping for Flood Routing in Steep and Densely Vegetated Areas: Insights from the Contok River Basin, Garang Watershed, Indonesia

This research utilizes photogrammetry to assess flood routing dynamics in the Contok river basin, a sub-watershed with a challenging landscape characterized by steep slopes, dense vegetation, and meandering patterns. The objectives are to assess Unmanned Aerial Vehicle (UAV) mapping accuracy, evaluate the river's capacity for design flood volumes, quantify the impact of land cover changes on surface runoff, and provide insights for early warning systems and watershed conservation strategies. The study area, encompassing the Contok River Basin, a sub-watershed of the Garang Watershed, covers 7,413 km² and includes a stream length of 5,274 meters in Semarang City, Central Java, Indonesia. This research employed image processing of aerial photographs and satellite imagery. Aerial photos captured using UAV data were utilized to derive elevation data and cross-sectional profiles of the Contok River, essential for understanding channel morphology and hydraulic characteristics. Concurrently, satellite imagery was used for land cover analysis, identifying vegetation and built-up areas that influence surface runoff dynamics. Hydrological analysis was performed to quantify discharge magnitudes, simulated against river cross-sections to evaluate flood behavior under varying scenarios. Our proposed UAV mapping provides adequate accuracy for small and local areas. Furthermore, it remains reliable for flood routing analysis. We discovered that the capacity of the Contok River channel in the downstream area allows it to convey design flood discharges up to a 50-year return period, contrary to the upstream area, it overflows. Notably, the shift from vegetated to built-up and agricultural areas significantly contributes to the 10.6% increase in surface runoff. This research highlights the role of UAV-based photogrammetry in assessing and mitigating flood hazards amidst evolving land cover patterns. It also enhances the understanding of flood dynamics and thus provides insights that will serve as a reference for flood early warning systems, flood management practices, and watershed conservation.

EGMS-toolkit: a set of Python scripts for improved access to datasets from the European Ground Motion Service

Continental-scale, open-access datasets of ground surface displacement in all countries of the European Union, plus Norway, United Kingdom, and Iceland, are now available from the European Ground Motion Service (EGMS). Under the European Union’s Copernicus program, the EGMS has been available since the end of 2022 and will continue for the foreseeable future. The EGMS data are presently derived from Interferometric Synthetic Aperture Radar (InSAR) processing of the Sentinel-1 SAR satellite imagery, which has been collected from 2015 to date. While EGMS data can be visualised and obtained through an online platform (EGMS Explorer), the data access arrangements are inefficient for large-scale analysis of ground surface displacements due to the volume of data, the tile-formatting of datasets and some server limitations. Here we present a Python-based toolkit, named EGMS-toolkit, to provide a unified and more efficient workflow for accessing EGMS datasets. The toolkit can automatically detect and download EGMS datasets based on a Region of Interest provided by users, then it can merge, clip, and crop the results to that region regardless of its scale. The toolkit then produces files of EGMS ground surface motions in GIS-ready formats for further analysis.

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

Cloud cover assessment is crucial for meteorology, Earth observation, and environmental monitoring, providing valuable data for weather forecasting, climate modeling, and remote sensing activities. Depending on the specific purpose, identifying and accounting for pixels affected by clouds is essential in spectral remote sensing imagery. In applications such as land monitoring and various remote sensing activities, detecting/removing cloud-contaminated pixels is crucial to ensuring the accuracy of advanced processing of satellite imagery. Typically, the objective of cloud masking is to produce an image where every pixel in a satellite spectral image is categorized as either clear or cloudy. Nevertheless, there is also a prevalent approach in the literature that yields a multi-class output. With the progress in Machine and Deep Learning, coupled with the accelerated capabilities of GPUs, and the abundance of available remote sensing data, novel opportunities and methods for cloud detection have emerged, improving the accuracy and the efficiency of the algorithms. This paper provides a review of these last methods for cloud masking in multispectral satellite imagery, with emphasis on the Deep Learning approach, highlighting their benefits and challenges.

Characterizing the local climate of large-scale archaeological parks in the tropics

PurposeThis study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.Design/methodology/approachThe study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.FindingsThe study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.Practical implicationsBased on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.Originality/valueThe local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

Kulla‐RIV: A composing model with integrity verification for efficient and reliable data processing services

AbstractThis article presents the design and implementation of a reliable computing virtual container‐based model with integrity verification for data processing strategies named the reliability and integrity verification (RIV) scheme. It has been integrated into a system construction model as well as existing workflow engines (e.g., Kulla and Makeflow) for composing in‐memory systems. In the RIV scheme, the reliability (R) component is in charge of providing an implicit fault tolerance mechanism for the processes of data acquisition and storage that take place in a data processing system. The integrity verification (IV) component is in charge of ensuring that data transmitted/received between two processing stages are correct and are not modified during the transmission process. To show the feasibility of using the RIV scheme, real‐world applications were created by using different distributed and parallel systems to solve use cases of satellite and medical imagery processing. This evaluation revealed encouraging results as some solutions that assumed the cost (overhead) of using the RIV scheme, for example, Kulla (the Kulla‐RIV solution), achieve better response times than others without the RIV scheme (e.g., Makeflow) that remain exposed to the risks caused by to the lack of RIV strategies.

Lithological discrimination of the Fawakhir-Atalla belt in the Central Eastern Desert of Egypt based on Landsat-9 remote sensing data, airborne gamma-ray spectrometry, field and petrographic investigations with implications on the evolution of the Arabian Nubian Shield

The Central Eastern Desert (CED) is a key territory for understanding the evolution of the Arabian Nubian Shield (ANS) in Egypt as it contains Cryogenian-Tonian ensimatic supracrustal assemblages, such as ophiolitic and arc volcanic rocks, as well as gold deposits. So, it has been the subject of extensive studies over the last two decades. However, the lithological mapping of the Fawakhir-Atalla belt, located west of the Meatiq gneiss dome (infrastructure) of the CED, is still debated, as the magmatic texture of some rocks was partially obscured owing to the shearing and alteration related to the Najd Fault System, mantle CO2-bearing fluid, and igneous activity in the final stage of the Pan-African Orogeny. In this regard, integrated studies of airborne gamma-ray spectrometry and Landsat-9 satellite imagery processing techniques, as well as field and petrographic investigations, have been used for the lithological mapping of the belt. The results led to the following sequence; ophiolitic rocks > island arc assemblages > older granitoids (syn-tectonic subduction-related) > Hammamat Group (molasse-type origin) > post-Hammamat felsite > younger granite (post-tectonic suture-related). The applied color composite images, selective band ratios, and image transformation techniques show a clear distinction between rock units. Older granitoids are tonalite to quartz diorite, while younger granites are monzo-to syenogranite. They are well-discriminated from each other using band ratios, and younger granites have the highest K, eU, and eTh values coherent with the post-Hammamat felsite. The image enhancement and image transformation techniques show a clear discrepancy between the mélange rocks and Hammamat conglomerates. Mélange rocks are composed of fragments embedded in a foliated matrix, while Hammamat conglomerates are grain-supported with pebbles of volcanic source and have higher K, eU, and eTh values. Volcanic rocks in the belt are not a part of the Dokhan series as they are metamorphosed with a composition range from meta-basalts to meta-rhyolite, and show mainly aphanitic to infrequently porphyritic textures, which are typical of island arc metavolcanics.

THE METHOD FOR OBJECTS DETECTION ON SATELLITE IMAGERY BASED ON THE FIREFLY ALGORITHM

The subject matter of the article is the method for detecting of objects on satellite imagery based on the firefly algorithm. The goal is to develop a method for detecting of objects on satellite imagery based on the firefly algorithm. The tasks are: analysis of existing methods for detecting of objects of interest on satellite imagery, development of a method for detecting of objects on satellite imagery, practical verification of the method for detecting of objects on satellite imagery based on the firefly algorithm, and quantitative assessment of the quality of the proposed method. The methods used are: methods of digital image processing, methods of data clustering, mathematical apparatus of matrix theory, methods of swarm intelligence, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The advantages and disadvantages of the main methods and approaches to the processing of satellite imagery for the purpose of detecting objects of interest on them are determined. The general principle of operation of the firefly algorithm is considered. It presents a flowchart of the method for detecting of objects on satellite imagery based on the firefly algorithm in one color channel. The values of the input data and parameters for the operation of the algorithm were selected experimentally. Experimental studies were conducted on the operation of the method for detecting of objects on a real satellite imagery based on the firefly algorithm. The values of the errors of the first and second kind for the processed image using the proposed method and the method based on the particle swarm algorithm were calculated. Conclusions. Analysis of the calculated values showed that the proposed method for detecting of objects on satellite imagery compared to the method based on the particle swarm algorithm: reduces the error of the first kind by about 11% and the error of the second kind by about 9%. The directions of further research are the study of the problem of selecting input parameters and data for the operation of the method based on the firefly algorithm.

Spatial and temporal disturbances in the zone of mining of ferruginous quartzites of the Kostomuksha cluster, NW Russia

The research presents new methods for analyzing spatial and temporal disturbances, focusing on landscape ecology. The study covers the period from the beginning of the development of the field to the present stage. The Land Use and Land Сover (LULC) change analysis is based on processing of Landsat satellite imagery. The method uses landscape metrics, SDI calculation, and mapping to describe the mechanisms of transformation and self-regulation of the system. Landscape metrics include three groups: areas and edges, shape metrics, and diversity metrics. An increase in landscape fragmentation over time is shown. All LULC classes have been changed. The area of the “mining" class has been increased in 7.7% (from 0 to 55.5 thousand km2). The increase in the area of quarries occurs evenly at the expense of all other land use classes. The data obtained confirm several stages of anthropogenic impact. An approach has been developed for mapping disturbances and assessing the impact on geosystemsIt was revealed that in the cell size range from 1500 to 2500 m, the landscape parameters are the most stable. The calculation of the spatial disturbance index is based on principal component analysis data. Factors were identified that indicate external load and the adaptation and self-regulation of the geosystem. Three degrees of disturbance of the territory are defined: low, medium, and high. In 2006, the maximum level of spatial disturbance was found, with the area of heavily disturbed areas being 68%. The results of this study indicate the importance of taking the necessary measures to reduce environmental risks.

Implementing the European Space Agency’s SentiNel Application Platform’s Open-Source Python Module for Differential Synthetic Aperture Radar Interferometry Coseismic Ground Deformation from Sentinel-1 Data

Differential SAR Interferometry is a largely exploited technique to study ground deformations. A key application is the detection of the effects promoted by earthquakes, including detailed variations in ground deformations at different scales. In this work, an implemented Python script (Snap2DQuake) based on the “snappy” module by SNAP software 9.0.8 (ESA) for the processing of satellite imagery is proposed. Snap2DQuake is aimed at producing detailed coseismic deformation maps using Sentinel-1 C-band data by the DInSAR technique. With this alternative approach, the processing is simplified, and several issues that may occur using the software are solved. The proposed tool has been tested on two case studies: the Mw 6.4 Petrinja earthquake (Croatia, December 2020) and the Mw 5.7 to Mw 6.3 earthquakes, which occurred near Tyrnavós (Greece, March 2021). The earthquakes, which occurred in two different tectonic contexts, are used to test and verify the validity of Snap2DQuake. Snap2DQuake allows us to provide detailed deformation maps along the vertical and E-W directions in perfect agreement with observations reported in previous works. These maps offer new insights into the deformation pattern linked to earthquakes, demonstrating the reliability of Snap2DQuake as an alternative tool for users working on different applications, even with basic coding skills.

Study of temporal changes in the hydrographic network of small mountain rivers in the Ile Alatau, Kazakhstan

Abstract The article presents the outcomes of an assessment of hydrographic network changes within the Almaty city, utilizing geographic information system (GIS) technology and Earth remote sensing data. Two gauge stations were selected along the main rivers within the Almaty city. To identify distinctive alterations in these rivers, hydrological data series encompassing the maximum runoff from 1970 to 2021 were collected and subjected to statistical analysis. Differential integral curves were constructed to pinpoint periods corresponding to peak and trough runoff levels. For each of these identified periods, the processing of satellite imagery allowed for the computation of meandering coefficients for the river channels. Additionally, refinements were made to the slope values of the rivers during these same timeframes, and connectivity graphs were established to elucidate the relationship between slope and liquid runoff for each period. The analysis encompassed an assessment of the impact of anthropogenic factors on both artificial and natural bodies of water while also considering shifts in the boundaries of the Almaty city. The findings derived from this study have practical applications in the planning and design of water supply systems and in the implementation of measures aimed at mitigating the adverse effects of anthropogenic factors on water bodies.

The High-Low Arctic boundary: How is it determined and where is it located?

Geobotanical subdivision of landcover is a baseline for many studies. The High-Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High-Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up-to-date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High-Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50-100 km further north compared to all the previously presented ones. Long-term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High-Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High-Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High-Low Arctic boundary in the circumpolar extent.

Dynamics of mangrove species dominant area changes in Timbulsloko and Bedono, Demak

Mangrove forests in Timbulsloko and Bedono villages have very important benefits in minimizing abrasion, due to tidal flooding and land absorption in this area. A large number of people have planted mangroves to restore the function of mangrove forests in coastal areas which conducted by NGOs, students, government agencies, and awareness from local residents. This study aimed to determine the dynamics of mangrove area and mangrove species dominant area changes in 2016, 2018, 2020 and 2022 based on Sentinel-2 Satellite Imagery processing, and to analyze the dynamic changes based on geospatial analysis. The method used in this study is divided into two: satellite imagery data processing and field survey. The result showed that the area of mangrove species in Timbulsloko and Bedono increased from 2016 to 2022. In 2016 the area of mangroves was 140.04 ha, 159.57 ha in 2018, 171.05 ha in 2020, and 234.8 ha in 2022. The use of Sentinel 2 Satellite Imagery to map the distribution of mangrove species dominant produce overall accuracy of 84.62%. The mangrove species with the highest area are Avicennia marina followed by Avicennia alba, Rhizophora apiculata, and Rhizophora mucronate. The increase in this area of mangroves in this area is due to natural additions and artificial additions due to mangrove planting conservation by several parties and the awareness of the local residents to protect mangroves.Keywords:Mangrove SpeciesDynamicsCoastal AreaSentinel 2Remote Sensing

Effects of Population Increase on Peri-Urban Land Growth in Asa Local Government Area, Kwara State

The rapid growth of world population and its agglomeration in cities and towns around the world is affecting the longterm outlook for humanity, in such that the process of urban growth and the effect at the peri-urban areas are universal, occurring all over the world. This study aimed at assessing the effects of population increase on the growth of periurban land in Asa local government area, Kwara State. Secondary data, via satellite imageries covering 2000, 2010 and 2021 were mainly used in analyzing the changes that occurred within twenty years. Remote sensing and GIS approaches to satellite imagery processing were adopted using ILWIS and ERDAS IMAGINE 9.2 software to subset the imageries, as well used for classification resampling. The result on built-up area reveals a consistence increase of over 80% between 2000 and 2021. The vegetation cover suffered a serious loss of vegetal land to the tune over 100 hectares due to various degree of development and expansion of the town. The water body also affected with loss in space covered with 80 hectares within the period under study. With a tremendous population increase, the study indicates a high demand for land and vegetal resources, which in turns possess a serious threat to food production. The study concludes that population increase remains the most significant determinant of peri-urban changes with a resultant effect on the wellbeing of peri-urban residents and rural dwellers. It therefore, recommends the adoption and application of strategic physical planning approach in the development of the peri-urban areas.

Changes in density level and mangrove land cover on Teluk Pandan Coast, Lampung, Indonesia, after 10 years of community-based management

Abstract. Hasani Q, Anisa A, Damai AA, Yuliana D, Yudha IG, Julian D. 2023. Changes in density level and mangrove land cover on Teluk Pandan Coast, Lampung, Indonesia, after 10 years of community-based management. Biodiversitas 24: 3735-3742. The mangrove ecosystem in the Teluk Pandan coastal area, Pesawaran District, Lampung Province, Indonesia, is part of the mega-biodiversity of mangroves in Indonesia. Specifically, the mangrove in this region has changed due to tourism, aquaculture, settlement activities and coastal abrasion. This study aimed to assess the changes in density level and area change of the mangrove forests at five coastal villages in Teluk Pandan during the period of community management from 2010 to 2020. The effort and success of the rehabilitation were reported in this study can be useful as a consideration of sustainable management of mangrove ecosystem both in Lampung and in other areas in Indonesia. Mangrove area was estimated through field surveys and data processing of satellite imagery from Landsat 5 in 2010 and Landsat 8 in 2020. The mangrove density level and area change were calculated by layering images of mangrove distribution from different years. The classification of mangrove density was determined based on the NDVI value. Satellite imagery was compared with field data collected from 20 observation points at five villages to ensure the density level and interpretation accuracy. Image data analysis showed the changes in mangrove area from 109.80 ha in 2010, an increase of 16.39% to 127.80 ha in 2020. The area of mangrove forest in the rare category decreased by 49.35%, the moderate category increased by 39.13%, and the dense category increased by 109.71%. The study confirmed the successful effort to manage and preserve mangroves in Teluk Pandan Sub-district. Eventually, community-based mangrove management (CBMM) and cross-sectoral and multi-stakeholder management (co-management) has become the core strategy in the successful management of mangrove at five coastal villages in Teluk Pandan Sub-district.

"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning"

Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.

A Comparative Analysis of Lineaments Extracted from Landsat 8 OLI & ASTER-GDEM Data, in Talmakent Area, Western High Atlas, Morocco

Structural mapping by remote sensing is an essential component to completing the geological mapping of the Talmaknet region. It allows us to understand the regional tectonics of the southernmost segment of the Hercynian of Western Morocco, exploiting Geographic Information Systems (GIS) and satellite imagery processing techniques in order to obtain a good lineament discrimination. To this end, the approach adopted was based on preliminary processing of Landsat 8 OLI satellite imagery combined with a morpho-structural shading analysis technique, using the Digital Elevation Model (DEM), and applying several techniques of image enhancement and directional filters to the different treatments to improve the visibility of the linear structures in the images.Given the interesting number of fractions collected, a statistical and directional study was carried out on the synthetic maps to show the distribution and orientation of the lineaments. This analysis indicates a dominant NE-SW oriented geological fracturing, which agrees with the existing geological data in the area, confirming the effectiveness of GIS and remote sensing techniques in structural geology studies, especially to have a detailed mapping of the linear structures in the study area.

Utilizing High Resolution Satellite Imagery for Automated Road Infrastructure Safety Assessments.

The European Commission (EC) has published a European Union (EU) Road Safety Framework for the period 2021 to 2030 to reduce road fatalities. In addition, the EC with the EU Directive 2019/1936 requires a much more detailed recording of road attributes. Therefore, automatic detection of school routes, four classes of crosswalks, and divided carriageways were performed in this paper. The study integrated satellite imagery as a data source and the Yolo object detector. The satellite Pleiades Neo 3 with a spatial resolution of 0.3 m was used as the source for the satellite images. In addition, the study was divided into three phases: vector processing, satellite imagery processing, and training and evaluation of the You Only Look Once (Yolo) object detector. The training process was performed on 1951 images with 2515 samples, while the evaluation was performed on 651 images with 862 samples. For school zones and divided carriageways, this study achieved accuracies of 0.988 and 0.950, respectively. For crosswalks, this study also achieved similar or better results than similar work, with accuracies ranging from 0.957 to 0.988. The study also provided the standard performance measure for object recognition, mean average precision (mAP), as well as the values for the confusion matrix, precision, recall, and f1 score for each class as benchmark values for future studies.

Monitoring of forest management. Prediction of area change

The current problems of monitoring of the state and changes in areas of green plantings are considered. The assessment of dynamics of the distribution of forest park plantations would allow making decisions on taking appropriate measures for forest restoration or sanitary logging to maintain the areas of the “lungs of the city” at the appropriate level. The analysis of global trends in the use of GIS and remote sensing to study the situation with green plantings shows that this allows obtaining up-to-date and reliable information promptly. Namely, it enables quick monitoring of ecological aspects on large areas and significantly speeds up taking necessary measures, if necessary. The available approaches to observe the tasks of monitoring of green areas are analysed. The existing problems of assessing the state of the forest park economy in complex environments such as cities are proposed to be solved by using software products such as ERDAS IMAGINE and ArcGIS for processing and further analysis of satellite imagery of the area. To solve the task of predicting the change in the forest park economy area over time, modelling linear regression is proposed using the built-in programming language in the ArcGIS. The developed application with an intuitive interface will provide an opportunity to conveniently work with databases and attribute information, graphics and images. The development includes five options for performing calculations, which allows analysing the results in each of the anticipated scenarios. All calculations were performed on the example of the Kharkiv Forest Park “Sokolniki-Pomirki”. An algorithm for processing and analysing satellite images was developed. The proposed algorithm can be applied to increase the speed of monitoring of the state of forests and predicting changes not only in Kharkiv, but also in other cities in Ukraine and worldwide. The paper demonstrates the capabilities of GIS for monitoring of green areas, which allow performing similar work with minimal time costs. Thus, the GIS product is promising at the current stage of observing changes in the areas of green plantings.