Medium Resolution Satellite Images Research Articles

Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.

Human bias and CNNs’ superior insights in satellite based poverty mapping

Satellite imagery is a potent tool for estimating human wealth and poverty, especially in regions lacking reliable data. This study compares a range of poverty estimation approaches from satellite images, spanning from expert-based to fully machine learning-based methodologies. Human experts ranked clusters from the Tanzania DHS survey using high-resolution satellite images. Then expert-defined features were utilized in a machine learning algorithm to estimate poverty. An explainability method was applied to assess the importance and interaction of these features in poverty prediction. Additionally, a convolutional neural network (CNN) was employed to estimate poverty from medium-resolution satellite images of the same locations. Our analysis indicates that increased human involvement in poverty estimation diminishes accuracy compared to machine learning involvement, exemplified with the case of Tanzania. Expert defined features exhibited significant overlap and poor interaction when used together in a classifier. Conversely, the CNN-based approach outperformed human experts, demonstrating superior predictive capability with medium-resolution images. These findings highlight the importance of leveraging machine learning explainability methods to identify predictive elements that may be overlooked by human experts. This study advocates for the integration of emerging technologies with traditional methodologies to optimize data collection and analysis of poverty and welfare.

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

The objective from this article evaluates the thematic quality of automatic mappings with supervised classification for land use and land cover, using high spatial resolution satellite images as "ground truth". This consider the advancement of remote sensing technologies has enabled the acquisition of satellite images with various spatial resolutions, which are essential for thematic mapping and automatic classifiers in the context of land use and land cover mapping. In fact, the wide availability of high, medium, and low spatial resolution satellite images has significantly optimized the time and resources required by using more accurate classifiers during data processing. The image used for verification of this paper was GeoEye, with a spatial resolution of 0.5m, dated October 2023. The images submitted to the automatic classifier were Sentinel-2A with a spatial resolution of 10m and Planet with a spatial resolution of 5m, both from the same satellite pass period (October 2023) over the study area, aiming to avoid seasonal and phenological variations in vegetation, as well as changes in the environment due to anthropogenic intervention. The classification method adopted was Maximum Likelihood (MAXVER). The classification accuracy was rigorously evaluated to ensure the reliability of the results using the Kappa index, assessing the agreement between the observed and expected classifications. Based on the methods presented, the set of mapped classes in this study showed good accuracy for the Planet image and very good accuracy for the Sentinel image.

VARIATIONS OF THE COASTLINE IN A MEDIUM PERIOD (1985-2017) OF A TROPICAL ISLAND: THE CASE OF SOURE (MARAJÓ - PARÁ/BRAZIL)

The margins of Marajó Island, influenced by large estuaries (rivers Amazonas and Pará), are con- ditioned to constant morphological changes. This article shows the multitemporal dynamics of the coastline (1985-2017 - 32 years) and the quantification of the areas in accretion and erosion in the Northern Margin (Sector I), Cabo Maguarí (Sector II) and Eastern Margin (Sector III) of the Soure municipality. The method- ology included the acquisition of medium resolution satellite images LANDSAT 5 TM (bands 5, 4 and 3 - 1985) and LANDSAT 8 OLI (bands 6, 5 and 4 - 2017), orthorectified and georeferenced, with spatial resolu- tion of 30m. They were processed using the ArcToolbox tool. The visual interpretation of the images and the vectorization of the coastline were done in ArcMap, using the ArcGis 10.6 software. The overlapping of the vectors of the years 1985 and 2017 allowed the caCLulation of areas and rates. The results show the predom- inance of the accretion of the coastline. The under accretion totaled 25,382,783.18 m² ± 575.28 m² (4.72 m year-1) and mean linear advance of the coastline of 151.10 m. However, the eroded areas totaled 6,696,780.64 m² ± 575.28 m² (3.03 m year-1) and the mean linear retreat of the coastline was 97 m.

Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.

Enhanced mangrove index: A spectral index for discrimination understorey, nypa, and mangrove trees

Unsupervised classification using vegetation indices has been extensively employed to map mangrove forests using medium-resolution satellite images. However, its capability is restricted to determining the extent of mangroves only. This study introduces a new spectral index called the enhanced mangrove index (EMI) for accurately mapping different components of mangrove vegetation, including mangrove trees, nypa, and understorey. An immediate effort is required to monitor the invasion of nypa and understorey in the mangrove forest of Segara Anakan Lagoon, located in Central Java, Indonesia. This issue may also be prevalent in other mangrove areas worldwide. The development of EMI involved: 1). the analysis of the reflectance exhibited by different types of mangrove vegetation, and 2). The performance of EMI was evaluated by comparing it with spectral indices such as automated mangrove map and index (AMMI), as well as supervised classification models like random forest (RF). The accuracy assessment indicates that the overall accuracy and Kappa coefficient achieved values of 0.87 and 0.84, respectively, surpassing other spectral indices and supervised classification models. AMMI and RF exhibited high overall accuracy, with values of 0.82 and 0.73, respectively. Additionally, they demonstrated a Kappa coefficient of 0.77 and 0.66, respectively.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.

Дослідження стану лісів Прикарпатського регіону за космічними зображеннями з використанням методу контрольованої класифікації

The purpose of the research is to monitor the forest objects of the Rava-Ruska forestry and determine changes in their areas from 2002 to 2022. The methodology of monitoring the state of the forests of the Rava-Ruska forestry is based on the using of multi-temporal medium-resolution satellite images with their subsequent processing using geographic information systems. For processing, we use images from Landsat 7 (July 2002) and Sentinel-2 (August 2022). We use csupervised classification using the maximum likelihood method. To post-process the classification results, we use Majority Filter. Based on the results, we calculate the area of classes for the relevant years. We perform a comparative analysis of these areas to determine changes in forest objects. Results. The research was conducted on a part of the territory of the Rava-Ruska forestry. Training samples were created for the following objects: deciduous and coniferous forest, fresh deforestation and overgrown deforedtation, open ground and roads, water and agricultural land. Histograms and scatter plots are used to evaluate the training samples. The suoervised classification was carried out using the maximum likelihood method on a part of the forestry territory with further post-processing. As a result of using GIS tools, a comparison of forest changes over two decades was made. The monitoring of the forests of the Rava-Ruska forestry allowed us to identify changes in forest objects, in particular deforestation, which in 2022 amounted to 679.2 hectares. Scientific novelty and practical significance. A methodology for monitoring forests using remote sensing materials has been developed. The possibilities of detecting changes in forest objects using the method of supervised classification are investigated.

Assessing the compatibility of EPR rates with one-dimensional numerical modelling in monitoring shoreline kinematics along with supplying near/long-term forecasts

The utilization of remote sensing technologies in conjunction with the Digital Shoreline Analysis System (DSAS) is a commonly employed approach for monitoring shoreline alterations. However, the reliability of DSAS as a predictive tool for assessing short-term and long-term shoreline changes remains questionable. Considering this uncertainty, there arose an urgent need to investigate whether DSAS can be utilized in conjunction with a one-dimensional numerical model, such as LITPACK, to evaluate its credibility over extended periods of 30 and 80 years. For this investigation, a specific area of the shoreline near the Kitchener drain in the Delta region of Northern Egypt was selected. Medium-resolution satellite images, rectified between 1987 and 2022, were employed to undertake various kinematic data processing tasks, including area classification, shoreline digitization, uncertainty estimation, and quantification of temporal changes using the EPR, LRR, and NSM tools. The obtained results were compared between DSAS and LITPACK for the shoreline coordinates of the study area, projecting the analysis until the years 2050 and 2100. The comparison revealed the presence of minor discrepancies in the DSAS outputs for shoreline prediction within the year 2050. However, these discrepancies significantly worsened as the year 2100 approached, casting doubt on the reliability of DSAS as a long-term forecasting tool. Simultaneously, this research provided implicit evidence supporting the use of DSAS in the formulation of a decision matrix for coastal zone planning with a duration of up to 30 years, as it demonstrated a degree of dependability within this timeframe.

Estimativa da Evapotranspiração da Soja Pelo Modelo SSEBop Utilizando Imagens de Alta Resolução Obtidas Por Aeronave Remotamente Pilotada

Abstract Evapotranspiration (ET) is one of the most important processes in the hydrologic cycle, constituting the main responsible for water losses at the surface. Several evapotranspiration models use information from surface temperature and vegetation indices captured by remote sensors such as MODIS and LANDSAT to estimate the ETc value. The objective of this study is to apply SSEBop model to estimate ETc of soybean in a field experiment under four water regimes, using high-resolution multispectral and thermal images collected from remotely piloted aircraft (RPA). Surface temperature and NDVI maps were generated as sources for evapotranspiration estimation. From a Python script, spatial variability maps of ETc were generated at different phenological stages of the crop. The quality of the model for ETc estimates was performed by comparing the modeling results with leaf transpiration data measured in the field using an infrared gas analyzer, whose results showed a good correlation (R2 = 0.76). These results demonstrated the possibility of transferring a model originally developed for processing low to medium-resolution satellite images to high-resolution spatial-temporal images acquired by RPA with small adaptations in the original algorithm, generating great potential for new studies on an experimental and field scale.

Mapping and Modelling of Land Use Change in Nepal

Land use changes are a common occurrence resulting from different factors in Nepal. There is a need for better understanding of the temporal and spatial change dynamics of land uses as well as their future projections. This paper assesses the land use change and its impact on carbon stocks and provides future scenarios of land use changes and their impacts on carbon stocks. The methods involved classification of medium resolution satellite image using Object Based Image Analysis (OBIA) techniques, analysis of the change across three time frames,modelling the future land use, and assessment of the impacts on carbon stocks. OBIA was used for the image segmentation and image classification for the assessment of land uses present in two different periods i.e. 2010 and 1990. The classification of the images was carried out using the spectral characteristics of the Landsat images. Image indices, i.e., Vegetation Index were used for land use classification.
 The land use changes over time and space was analysed using Geographical Information System (GIS) overlaying techniques. A future scenario model was prepared using Land Change Modeler of IDRISI software. The study has focused on the land use changes and their future projection based on the potential transition of the land uses. The comparison of land use maps from 1990 to 2010 shows that the different pattern of annual land use changes in different districts such as forest areas are increasing at annual rate of 0.3 per cent in Rasuwa, 2.3 percent in Gorkha and 0.6 per cent in Chitwan. Similarly, the agricultural land is decreasing at the annual rate of 0.2 per cent in Rasuwa, 2.4 per cent in Gorkha and 0.8 per cent in Chitwan. Likewise, the future projection of land uses in all the three sites was made using the previous land use pattern and driving forces. There was a 0.1 per cent change annually in forest coveragein both Gorkha and Rasuwa whereas Chitwan showed higher rate of forest cover change of 0.2 per cent annually.

Improving agricultural field parcel delineation with a dual branch spatiotemporal fusion network by integrating multimodal satellite data

Accurate spatial information for agricultural field parcels is important for agricultural production management and understanding agro-industrialization and intensification. However, traditional remote sensing methods that rely on single-modal or single-date data struggle to identify heterogeneous field parcels, particularly in regions dominated by smallholder farming systems. To address this challenge, we proposed a Dual branch Spatiotemporal Fusion Network (DSTFNet) that integrated very high-resolution (VHR) images and medium-resolution satellite image time series (MRSITS) to extract agricultural field parcels over various landscapes. The DSTFNet consisted of two branches: a spatial branch that extracted spatial features from VHR images and a temporal branch that explored seasonal spectral dynamics from MRSITS data by using ConvLSTM units and an attention module. We evaluated the DSTFNet in four regions across China by using GF-2 and Sentinel-2 data. The results showed that DSTFNet performed well in delineating agricultural field parcels, achieving the highest Matthew’s correlation coefficient (MCC) = 0.823 for the field extent, the highest F1-score of edge (Fedge) = 0.865 for field boundary, and the lowest errors of segmentation evaluation index (SEI) = 0.191 for the vectorized field parcels in Hubei province. In addition, DSTFNet significantly outperformed two single-branch models that used VHR or MRSITS alone, as well as existing BsiNet, ResUNet_a, UNet and RAUNet models. DSTFNet also showed good spatial transferability in distinct regions without training data (on average, MCC = 0.728, Fedge = 0.729, and SEI = 0.281 for three target regions). Using limited training data to fine-tune the DSTFNet can further improve its ability to delineate field parcels over complex regions. The visualization analysis of temporal attention weights demonstrated that DSTFNet can well capture cropland spectral dynamics, making it advantageous in extracting diverse cropland parcels. By exploiting important spectral, spatial and temporal information from multimodal satellite data, DSTFNet provided an effective, robust, and transferable solution for accurately delineating agricultural field parcels across heterogeneous farming systems.

Glacier retreat, dynamics and bed overdeepenings of Parkachik Glacier, Ladakh Himalaya, India

Abstract This study describes the morphological and dynamic changes of Parkachik Glacier, Suru River valley, Ladakh Himalaya, India. We used medium-resolution satellite images; CORONA KH-4, Landsat and Sentinel-2A from 1971–2021, and field surveys between 2015 and 2021. In addition, we used the laminar flow-based Himalayan Glacier Thickness Mapper and provide results for recent margin fluctuations, surface ice velocity, ice thickness, and identified glacier-bed overdeepenings. The results revealed that overall the glacier retreated by −210.5 ± 80 m with an average rate of 4 ± 1 m a−1 between 1971 and 2021. Whereas a field study suggested that the glacier retreat increased to −123 ± 72 m at an average rate of −20 ± 12 m a−1 between 2015 and 2021. Surface ice velocity was estimated using COSI-Corr on the Landsat data. Surface ice velocity in the lower ablation zone was 45 ± 2 m a−1 in 1999–2000 and 32 ± 1 m a−1 in 2020–2021, thus reduced by 28%. Further, the maximum thickness of the glacier is estimated to be ~441 m in the accumulation zone, while for glacier tongue it is ~44 m. The simulation results suggest that if the glacier continues to retreat at a similar rate, three lakes of different dimensions may form in subglacial overdeepenings.

Exploring the largest known Bronze Age earthworks in Europe through medium resolution multispectral satellite images

This study aims to provide new insights into Europe’s largest known Bronze Age earthworks using open-access and freely distributed medium resolution satellite images. The most extensive Bronze Age fortifications in Europe, namely, the Corneşti-Iarcuri and Sântana – Cetatea Veche sites, were investigated through the Sentinel 2 and the newly launched Landsat 9 optical sensors. Image processing techniques were applied to both datasets, including vegetation indices, orthogonal spectral transformations, and pan-sharpening techniques. The final results revealed several known and unknown archaeological proxies by enhancing a number of linear and curved crop marks in the vicinity of the archaeological sites. Indeed, while previously implemented geophysical results confirmed some of these archaeological proxies, new findings (crop marks) were also revealed, representing archaeological structures that were unknown until now. The study's overall findings indicate that medium resolution satellite images can be used in appropriate areas with archaeological interest as a first step toward better understanding the broader context of an area. The findings addressed in this study have a direct impact on the non-invasive aspect of archaeology, as the methodology employed in this paper may be applied to various types of sites in southwestern Romania and beyond and might serve as a solid starting point for any archaeological project. Finally, this is the first elaboration of Landsat 9 intended for archaeological research and our study proves that its utility for archaeological and heritage purposes.

The Glacial Meltwater Turbidity Algorithm (GaMTA): Adaptation of single-band algorithm retrieving turbidity to satellite and UAV dataset from highly glaciated Antarctic region

The growing need for marine polar observations, especially using remote sensing data, necessitates the adaptation of existing algorithms to local conditions. Five satellite imageries acquired by MSI and OLI sensors from 2020 and 2021 depicting Admiralty Bay in King George Island and sixteen multispectral maps created using data from Unoccupied Aerial Vehicles (UAV) flights with a spectral camera over the cove near Zalewski Glacier were analysed. Data were supplemented by field measurements of turbidity. The Glacial Meltwater Turbidity Algorithm (GaMTA) was proposed and compared with well-known semianalytical, single-band algorithms for turbidity retrieval based on spectral imageries of heavily glaciated areas. Comparative analysis showed the ineffectiveness of algorithms applied to medium-resolution satellite images in the area of small glacial coves with high and spatially varying surface water turbidity (R = 0.59-0.61, RMSE = 4.45-7.46), in contrast to open water with low turbidity (R = 0.79-0.88, RMSE = 2.61-3.08). The results from the use of high-resolution UAV data showed the applicability of this algorithm to aerial images (R = 0.96-0.97, RMSE = 5.75-7.57). The use of GaMTA, which is adapted to local conditions, produces better results than the other algorithms, but validation on a larger dataset is needed.

Reconstruction of post-little ice age glacier recession in the Lahaul Himalaya, north-west India

ABSTRACT Understanding past glaciation and deglaciation is vital for assessing present-day glacier dynamics and response to climate change. We focus on reconstructing past glacier fluctuations in Lahaul, north-west India, a region located between arid Ladakh and the humid the Pir-Panjal range. We focus specifically on six glaciers in the Miyar and Thirot catchments of varying size, aspect and debris cover. To reconstruct past terminus fluctuations of these glaciers, we used repeat terrestrial photography and historical archives as data sets and mapped the terminus positions and latero-terminal moraines in the field along with glacier terminus mapping from high to medium resolution satellite images (e.g. Corona, Hexagon, Landsat and LISS IV). Results show that since the little ice age, all the studied glaciers have experienced terminus retreat and area loss, with average values of 1.46 and 0.9 km2, respectively. Precipitation data show a statistically significant decreasing trend during the last century with an increasing trend in annual average maximum (T max) and minimum (T min) temperature. This warming trend is more statistically significant for T min. Although total ice loss at the six studied glaciers is considerable (5.48 km2), this varies both spatially (i.e. from glacier to glacier) and temporally. We attribute this variability to topographic controls such as glacier hypsometry and another non-climatic factor, i.e. varying degree of debris cover.

Associations of exposures to air pollution and greenness with mortality in a newly treated tuberculosis cohort.

Some previous studies had linked air pollutants and greenness to the risk of death from tuberculosis (TB). Only a few studies had examined the effect of particulate matter (PM2.5) on the mortality of TB, and few studies had assessed the impact and interaction of multiple air pollutants and greenness on the mortality of newly treated TB patients. The study included 29,519 newly treated TB patients from three cities in Anhui province. We collected meteorological data and five pollutants data from The National Meteorological Science Center and air quality monitoring stations. Greenness data were generated by remote sensing inversion of medium-resolution satellite images. We geocoded each patient based on the residential address to calculate the average exposure to air pollutants and the average greenness exposure for each patient during treatment. The Cox proportional risk regression model was used to evaluate the effects of air pollutants and greenness on mortality in newly treated tuberculosis patients. Our results found that the higher the concentration of air pollutants in the living environment of newly treated TB patients, the greater the risk of death: HR 1.135 (95% CI: 1.123-1.147) and HR 1.333 (95% CI: 1.296-1.370) per 10μg/m3 of PM2.5 and SO2, respectively. Greenness reduced the mortality among newly treated TB patients: HR for NDVI exposure 0.936 (95% CI: 0.925-0.947), HR for NDVI_250m exposure 0.927 (95% CI: 0.916-0.938), and HR for NDVI_500m exposure 0.919 (95% CI: 0.908-0.931). Stratifying the cohort by median greenness exposure, HRs for air pollutants were lower in the high greenness exposure group. Mortality in newly treated TB patients is influenced by air pollutants and greenness. Higher green exposure can mitigate the effects of air pollution. Improving air quality may help reduce mortality among newly treated TB patients.

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.

Mapping of NDVI dynamics based on spatial images of medium resolution on the territory of the North Caucasus federal district

The article presents the possibilities of mapping dynamics of the normalized differential vegetation index (NDVI) onto the territory of the North Caucasian Federal District based on Russian satellite images of medium resolution. In the course of our work, the statistical weather data were processed and analyzed based on the results of long-term ground-based observations at some weather stations. The methods of using remote sensing data for mapping using GIS technologies were explored and NDVI index maps were compiled. Using remote sensing data and the calculated vegetation indices makes it possible to successfully map various processes and phenomena; the maps compiled in this way can be used to solve a wide range of scientific and practical problems.

Sustainability of Boro rice cultivation in the canal irrigated command area of India

Abstract A comprehensive assessment and monitoring of rice intensification in canal irrigated command areas and its consequences on groundwater dynamics have enormous importance for the water–food–energy nexus. This study analyzed the spatiotemporal transition of Boro rice cultivation, canal irrigation practices during the cultivation season, and its effect on groundwater of a pioneering multipurpose water resource project in India. The rice maps from 1989 to 2018 were generated from Landsat and Sentinel-2A images using the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA), the normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI) to understand the historical distribution of rice cultivation. The ISODATA clustering algorithm performed well compared to the NDVI and the EVI with medium-resolution satellite images. The impact of Boro rice cultivation on groundwater resources was investigated by considering GLDAS NOAH model-based crop evapotranspiration, rainfall, and canal water supply. The area under rice cultivation increased from 33 to 66% during 1989–2018 due to its higher stress tolerance ability and productivity. However, during the cultivation period, the canal water supply was limited to the upstream and middle section of the command area, irrigating only 19% of the rice-cultivated area on average. The unavailability of the canal water supply during the cultivation season has led to 31 m of groundwater level depletion, which, if unchecked, may lead to overexploitation of groundwater in the middle and lower sections of the command area. The results of this study can be used to identify vulnerable areas that require urgent attention and accurate implementation of any agricultural or hydrological policy.