Sentinel-2 Satellite Images Research Articles

Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems.

Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content.

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.

Generation of PM10 Map with Sentinel-2 Satellite Images: The Case of Çankırı Province

Air pollution, which is characterized as a global environmental problem, negatively affects life in Turkey as a result of the increase in the amount of energy needed and uncontrolled construction. In order to minimize the health impacts of air pollution, air quality should be monitored regularly and necessary steps should be taken to improve it. With the innovations in satellite technologies, the air quality of large areas can be monitored with the help of satellite images and effective solutions can be produced in many areas such as the detection of air pollutant parameters and the creation of thematic maps. The main objective of the research is to investigate the relationship between in-situ measured PM10 parameters and Sentinel-2 satellite data and to map PM10 based on this relationship. In this context, PM10 parameters measured in the field on two different dates and Sentinel-2 satellite images dated 22.11.2021 and 16.04.2022 were used as data sources. The relationship between the data used was established by multiple regression analysis. The coefficients obtained from the analysis results were applied to the relevant bands and thematic maps were created using satellite images. The correlation coefficients of 0.80 and 0.79 calculated by regression analyses indicate that sufficient accuracy was achieved in the research. The results of the study show that satellite imagery provides accurate data for PM10 estimate and that pollution exceeds World Health Organization limits in the road transportation network and industrial areas.

Inversion of Soil Moisture Content in Cotton Fields Using GBR-RF Algorithm Combined with Sentinel-2 Satellite Spectral Data

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study’s demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study’s findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency.

Using vegetation and chlorophyll indices to model above ground biomass over time in an urban arboretum in subtropical queensland

As urban forests grow, they can provide a range of ecosystem services, including by sequestering carbon in the form of above ground biomass (AGB). Data from satellites is starting to be used to estimate ecological functions of urban forests including AGB. In this study, we compare individually and in combination five vegetation index and three chlorophyll specific indices based on Sentinel-2 satellite images in spring (September to November) of a newly planted multi-species arboretum over eight years in subtropical Queensland, Australia. There was variation in the eight indices within the arboretum, but some clear patterns among indices over time reflecting the growth of the forest. Using a published simple regression model of AGB as a function of Leaf Area Index (LAI), we found Modified Soil Adjusted Vegetation Index (MSAVI) and Modified Chlorophyll Absorption Ratio Index (MCARI) best predicted AGB (r-square = 0.52 and r-square = 0.84, respectively), and in combination gave the best estimate (r-square = 0.85). The performance of these chlorophyll-specific indices (MSAVI and MCARI) reinforces their benefits over traditional vegetation indices in characterizing vegetation patterns and could complement field-based sampling when modelling carbon sequestration potentials including in mixed species urban forests.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.

Identification of Pine Wilt Disease-Infested Stands Based on Single- and Multi-Temporal Medium-Resolution Satellite Data

Pine wilt disease (PWD) is known for its high lethality and rapid transmission, earning it the name “cancer of the pine tree”. The prompt removal of infested pine trees is an effective measure for preventing and controlling pine wilt disease. Accurate and efficient monitoring technologies are crucial for the scientific prevention and control of this plant disease. Currently, numerous remote sensing monitoring studies have been conducted on pine wilt disease. However, there is limited research on the temporal identification of PWD-infested forest stands over large areas. To build classification models, this study utilized three machine learning algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). We aimed to investigate the effectiveness of single-temporal and multi-temporal Landsat and Sentinel-2 satellite images PWD-infested forest stands detection. The results indicated that, at a spatial resolution of 30 m, Landsat-9 and Sentinel-2 remote sensing images effectively identified PWD-infested forest stands, with classification accuracies of 77.87% and 78.91%, respectively. Higher spatial resolutions in Sentinel-2 remote sensing images were associated with improved identification capabilities. Furthermore, multi-temporal Landsat satellite data (with a classification accuracy of 85.95%) significantly enhanced the performance of the monitoring model compared to single-temporal Landsat satellite data (with a classification accuracy of 77.87%). The RGI difference was found to be the optimal vegetation index. In conclusion, by combining multi-temporal and single-time-phase Landsat remote sensing data, a monitoring model for PWD-infested forest stands was constructed. It achieved a classification accuracy of 88.26%. In this study, a higher accuracy in identifying pine wilt disease and a lower economic cost were achieved by Landsat and Sentinel images, offering valuable insights for the management of pine wilt disease.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

Assessing the impact of cyclonic storm Tauktae on shoreline change in Mangaluru coast using geospatial technology

Cyclones are natural events that occur with atmospheric pressure variations. Compared to the eastern coast of India, the western coast has less cyclonic activity. Even though some of the cyclonic events turn into severe cyclonic storms and cause a huge impact on ecological and anthropogenic activities. This research focuses on the impact of cyclone Tauktae on the Mangalore coast. Cyclone Tauktae, one of the most powerful to pass through coastal Karnataka, caused significant damages, affecting 121 villages and resulting in four casualties. Utilizing geospatial techniques, this study employs Sentinel-2 satellite images to analyze shoreline changes during pre and post-cyclone Tauktae. A GIS technique involves the extraction of shorelines using 10-meter resolution images, with manual digitization excluding natural and man-made structures. Transect lines are generated with the help of DSAS to measure shoreline movement, distinguishing accretion and erosion. Rainfall conditions during the cyclone period are mapped using gridded rainfall data. The study area, encompassing ten coastal villages from Someshwara to Sasihithlu, is characterized by its vulnerability to cyclones. Results reveal substantial shoreline changes, with notable erosion in central and northern Mangaluru, Tannirbavi, and Hosabettu. Accretion is observed in south Someshwara and north Sashihitlu. Rainfall patterns are analyzed using Persian CCS gridded data, depicting the dynamic nature of precipitation during the cyclonic event. This research contributes valuable insights into the short-term effects of cyclonic storms on shorelines, emphasizing the importance of geospatial techniques for coastal management. The findings serve as a foundation for mitigating cyclonic impacts and enhancing resilience in the Mangalore coast, aiding future coastal management strategies

A COMPARATIVE STUDY OF SATELLITE IMAGE RESOLUTIONS FOR DETECTING PEST DAMAGE IN SUNFLOWER FIELDS

Abstract. The diversity of sensors in remote sensing allows for faster and easier detection of changes and issues across different scales, in contrast to conventional ground-based systems. One of the most important distinguishing features among these sensors is their varying resolutions, contributing to the versatility of remote sensing technologies across diverse environmental applications. In this study, the effectiveness of PlanetScope and Sentinel-2 satellite images with different image resolutions in detecting damage caused by a harmful insect (beet webworm moth - Loxostege sticticalis) in sunflower fields in Lüleburgaz district of Kırklareli in the Trace region was evaluated. Damage rates in sunflower fields were analyzed using various spectral indices (Enhanced Vegetation Index and Chlorophyll Index Green) and spectral transformation (Tasseled Cap Greenness) in conjunction with in situ data. Based on the spectral analysis, the satellite image dated 26 July, which showed the most severe damage, was used in the damage assessment analysis. The damaged areas were compared by classifying both satellite images with the Random Forest algorithm. According to the results of the classification accuracy assessment, PlanetScope satellite imagery showed the highest accuracy, with 90% overall accuracy and 84% Kappa statistics, making it a more suitable sensor choice for agricultural applications.

FOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGES

Abstract. Forests are invaluable for maintaining biodiversity, watersheds, rainfall levels, bioclimatic stability, carbon sequestration and climate change mitigation, and the sustainability of large-scale climate regimes. In other words, forests provide a wide range of ecosystem services and livelihoods for the people and play a critical role in influencing global atmospheric cycles. Providing sustainable, reliable, and accurate information on forest cover change is essential for an holistic forest management, efficient use of resources, neutralizing the effects of global warming and better monitoring of deforestation activities. Within the scope of this study, it is aimed to perform semantic segmentation of 5 different tree species (larch, red pine, yellow pine, oak, spruce) from Sentinel-2 satellite images. For this purpose, the regions where these tree species are densely populated in Turkey (Marmara, Aegean, Eastern Black Sea) were selected as pilot regions. A unique data set was created using the data of the selected pilot regions. As a result of the study, it was possible to determine the forest types temporally for the selected classes with more than 90% Intersection over Union score for all classes. The developed deep learning model with the created forest data set can be implemented to the other forests areas with same species in other parts of the world.

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

ContextWildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.ObjectivesWe demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.MethodsUsing Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.ResultsWe document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.ConclusionWe demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia

Abstract Normalized difference vegetation index (NDVI) is one of the parameters of vegetation that can be studied by remote sensing of land surface with Sentinel-2 (S-2) satellite image. The NDVI is a nondimensional index that depicts the difference in plant cover reflectivity between visible and near-infrared light and can be used to measure the density of green on a piece of land. On the other hand, the dual-pol radar vegetation index (DpRVI) is one of the indices studied using multispectral synthetic aperture radar (SAR) images. Researchers have identified that SAR images are highly sensitive to identify the buildup of biomass from leaf vegetative growth to the flowering stage. Vegetation biophysical characteristics such as the leaf area index (LAI), vegetation water content, and biomass are frequently used as essential system parameters in remote sensing data assimilation for agricultural production models. In the current study, we have used LAI as a system parameter. The findings of the study revealed that the optical data (NDVI) showed a high correlation (up to 0.712) with LAI and a low root-mean-square error (0.0296) compared to microwave data with 0.4523 root-mean-square error. The NDVI, LAI, and DpRVI mean values all decreased between 2019 and 2020. While the DpRVI continued to decline between 2020 and 2021, the NDVI and LAI saw an increase over the same period, which was likely caused by an increase in the study area’s average annual rainfall and the cautious stance of the Red Global (RSG) project on sustainability.

Photoacclimation in the kleptoplastidic ciliate Mesodinium rubrum and its cryptophyte prey Teleaulax amphioxeia: phenotypic variability and implications for red tide remote sensing

Abstract Mesodinium rubrum is a kleptoplastidic ciliate that sequesters the chloroplasts and nuclei of cryptophyte algae to perform photosynthesis. Blooms of M. rubrum can cause red tides in coastal oceans worldwide. Such red tides are detectable by remote sensing, and studying M. rubrum pigments and optical properties is a crucial step toward characterizing its blooms using satellite observation. Previous studies have shown that M. rubrum photoacclimates, modifying its pigment content depending on irradiance. Using cultures at different irradiance levels, we observed that photoacclimation in M. rubrum closely resembles that of its cryptophyte prey Teleaulax amphioxeia, leading to substantial phenotypic variability. In both species, phycoerythrin 545 cellular concentrations increased 3-fold between the highest and lowest irradiance, suggesting a major role in photoacclimation. Absorption cross-section decreased, and pigment-specific absorption coefficients increased with irradiance at the peak absorption wavelengths of chlorophyll a and phycoerythrin 545. After assessing the variability of absorption properties in M. rubrum, we combined field measurements and high-resolution Sentinel-2 satellite images to estimate chlorophyll a concentration of a coastal red tide and document small-scale spatio-temporal features. This work provides an overview of pigment photoacclimation in a peculiar phytoplankter and suggests guidelines for future studies of M. rubrum blooms.

Satellite derived bathymetry for rapid investigation for possible navigational channel design for coastal wind farm installation: a case study at Silavathurai, Sri Lanka

A rapid bathymetric survey was required for the transportation of wind turbines and blades to the Silavathurai coastline, Sri Lanka via sea. This area is a shallow uncharted area which makes this task a challenge. To overcome this limitation, remote sensing techniques were used to derive the bathymetry of the area using Sentinel-2 satellite images because of its high-resolution capabilities. The empirical bathymetric method was used by incorporating band ratio techniques that involves comparing different bands of the satellite imagery in estimating water depths. Three band combinations (Green–Blue, Red–Blue and Red–Green) were used and evaluated for their effectiveness in estimating water depths. The findings showed varying degrees of correlations between the in-situ measurements and bathymetry values. Green–Blue band combination gave the strongest correlation (R2 = 0.91) among the band combinations, indicating that it is most suitable for bathymetry estimation in such situations. Further, various depth zones were also tested for correlation analysis, which reveals higher correlation values for shallower depths. Then, accuracy analysis was done based on the computed Root Mean Square Error (RMSE) values and Green–Blue combination gave the least overall RMSE value (1.06 m) with the measured depths. Finally, the derived bathymetry data from the satellite images played a vital role in designing the navigation channel, ensuring safe transport of wind turbines for the Silavathurai wind farm project. The study emphasises the effectiveness of the remote sensing approach in determining bathymetry for shallow areas, offering insightful information for coastal renewable energy projects.

Measuring the quality and accessibility of urban greenery using free data sources: A case study in Bratislava, Slovakia

The importance of assessing urban greenery is growing, especially in the context of global warming and the emergence of urban heat islands. The paper proposes a set of measurements to evaluate the quality and accessibility of greenery based on freely available geographic data: Sentinel-2 satellite images, Urban Atlas, and Open Street Map. The quality (or availability) of urban greenery is derived from Sentinel-2 images by using the spectral Forest Index (FI). The proposed Neighborhood Green Quality Indexes (NGQI0/30, NGQI350) evaluate greenery in the near and far surroundings of the spatial unit by averaging the FI values. The Accessibility to Green Space Index (AGSI) measures accessibility to green spaces (GS) based on the number of theoretical functional levels within reach and the average normalised distance to the nearest GS across levels. We also propose to separately assess accessibility to parks with amenities through the Accessibility to Urban Parks Index (AUPI). Such areas attract specific groups of the population, such as elderly people and mothers with children, who are among the most vulnerable during heat waves. The proposed set of measurements can be applied to a square grid and to buildings. A mutual comparison of the proposed measurements in Bratislava, the capital of Slovakia, confirmed that each measurement captures different characteristics of urban greenery. The comparison of FI with two other widely used green quality indicators showed that FI achieves the highest correlation with the land surface temperature during hot summer days. When evaluating the precision of the NGQI0/30 calculation, Sentinel-2-derived FI proved to be a sufficient substitute for high-resolution urban greenery data sources. Since the proposed measurements do not require complex calculations and hard-to-obtain data, they are easily applicable to other cities.

Early Mapping Method for Different Planting Types of Rice Based on Planet and Sentinel-2 Satellite Images

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R2 by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes.

Remote sensing based forest cover classification using machine learning

Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies.

Mangrove browning trends in the semiarid southern Caribbean: spatial context matters

Mangroves are tropical coastal ecosystems threatened mainly by land cover change due to commodities. The effect of urbanization has been overlooked due to the low percentage of area loss globally, but it can be a significant driver of mangrove loss locally. Moreover, highly fragmented mangrove areas dominated by small patches might be more sensitive to natural and anthropogenic stressors as in terrestrial forests, especially in highly modified systems such as cities. However, little is known about the interactive effects of these drivers on mangrove loss and degradation, namely urbanization and fragmentation. Our objective is to analyze the mangrove ecosystem’s response in terms of the greenness trend to urbanization and patch fragmentation in the semiarid Colombian Caribbean. Specifically, we aim to: (1) calculate the greenness trend from 2017 to 2023 during the dry season by analyzing the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 satellite images; (2) correlate the greenness trend in mangrove patches with fragmentation metrics at the patch level; (3) evaluate the patch greenness trend along an urban-to-rural gradient. Our analysis revealed that 89.5% of the area showed no significant trend, while 5.2% exhibited greenness trends and 5.3% showed browning trends, mainly concentrated along the Canal del Dique and the Sinú River delta. Patch shape and isolation did not seem to affect patch greenness trends. In-patch greenness trends variability followed a U-shaped pattern along the urban to rural gradient. These findings highlight the importance of spatial context on vegetation condition and suggest opportunities for remote monitoring of mangrove degradation.