Remote Sensing Satellite Data Research Articles

Detecting Variability and Analyzing Vineyards Vegetation Characteristics Using Satellite Remote Sensing Data in Aswan, Egypt

Detecting Variability and Analyzing Vineyards Vegetation Characteristics Using Satellite Remote Sensing Data in Aswan, Egypt

MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes

Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.

Spatio-temporal characteristics of urban heat Island of Jakarta metropolitan

Around 151 million people (56%) of Indonesia's population currently live in cities. When the inner city sees substantially warmer temperatures than neighboring rural areas, a city experiences the urban heat island (UHI) effect. For both day and night time, the inner city within the Jakarta area tends to have a higher land and surface air temperature in comparison to its surroundings. Using available remote sensing satellite data (MODIS and Landsat) and surface meteorological observations, this research detects and defines the spatiotemporal land surface (SUHI) development in the Jakarta agglomeration region. In this paper, how the intensity of land surface (SUHI) and surface air UHI (AUHI) is measured, the trend assessment of 20 years SUHI area, as well as how land use change contributes to UHI development in the Jakarta Metropolitan area, is discussed. The results show that the SUHI intensity is approximately 3°C–6°C and AUHI is approximately 1°C–2.5 °C. This paper offers a comprehensive feature of UHI in Greater Jakarta as a research novelty, which is getting worse in response to global warming.

Assessing Shoreline Changes in Fringing Salt Marshes from Satellite Remote Sensing Data

Salt marshes are highly important wetlands; however, external pressures are causing their widespread deterioration and loss. Continuous monitoring of their extent is paramount for the preservation and recovery of deteriorated and threatened salt marshes. In general, moderate-resolution satellite remote sensing data allow for the accurate detection of salt marsh shorelines; however, their detection in narrow and fringing salt marshes remains challenging. This study aims to evaluate the ability of Landsat-5 (TM), Landsat-7 (ETM+), and Sentinel-2 (MSI) data to be used to accurately determine the shoreline of narrow and fringing salt marshes, focusing on three regions of the Aveiro lagoon in Mira, Ílhavo and S. Jacinto channels. Shorelines were determined considering the Normalized Difference Vegetation Index (NDVI), and the accuracy of this methodology was evaluated against reference shorelines by computing the Root Mean Square Error (RMSE). Once validated, the method was used to determine historical salt marsh shorelines, and rates of change between 1984 and 2022 were quantified and analyzed in the three locations. Results evidence that the 30 m resolution Landsat data accurately describe the salt marsh shoreline (RMSE~15 m) and that the accuracy is maintained when increasing the spatial resolution through pan-sharpening or when using 10 m resolution Sentinel-2 (MSI) data. These also show that the salt marshes of the Ílhavo and S. Jacinto channels evolved similarly, with salt marsh shoreline stability before 2000 followed by retreats after this year. At the end of the four decades of study, an average retreat of 66.23 ± 1.03 m and 46.62 ± 0.83 m was found, respectively. In contrast to these salt marshes and to the expected evolution, the salt marsh of the Mira Channel showed retreats before 2000, followed by similar progressions after this year, resulting in an average 2.33 ± 1.18 m advance until 2022.

Changes in Snow Surface Albedo and Radiative Forcing in the Chilean Central Andes Measured by In Situ and Remote Sensing Data

Snow-covered regions are the main source of reflection of incident shortwave radiation on the Earth’s surface. The deposition of light-absorbing particles on these regions increases the capacity of snow to absorb radiation and decreases surface snow albedo, which intensifies the radiative forcing, leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work, the changes in surface snow albedo and radiative forcing were investigated, induced by light-absorbing particles in the Upper Aconcagua River Basin (Chilean Central Andes) using remote sensing satellite data (MODIS), in situ spectral snow albedo measurements, and the incident shortwave radiation during the austral winter months (May to August) for the 2004–2016 period. To estimate the changes in snow albedo and radiative forcing, two spectral ranges were defined: (i) an enclosed range between 841 and 876 nm, which isolates the effects of black carbon, an important light-absorbing particle derived from anthropogenic activities, and (ii) a broadband range between 300 and 2500 nm. The results indicate that percent variations in snow albedo in the enclosed range are higher than in the broadband range, regardless of the total amount of radiation received, which may be attributed to the presence of light-absorbing particles, as these particles have a greater impact on surface snow albedo at wavelengths in the enclosed band than in the broadband band.

Temporal and Spatial Characteristics of Troposphere O3 and Precursors HCHO and NO2 in East China

Based on OMI remote sensing satellite data, the temporal and spatial characteristics of tropospheric ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO) concentrations in East China from 2005 to 2021 were analyzed, and the backward trajectory (HYSPLIT) model was used to explore their sources. The results showed that ① during the 17 years, the tropospheric O3 concentration steadily increased, reached the maximum value in 2010, and then showed a fluctuating and undulating state. NO2 showed an increasing trend from 2005 to 2012 and slowly decreased from 2012 to 2021. The average HCHO concentration showed an increasing trend from 1.15×1016molec·cm-2 in 2005 to 1.8×1016 molec·cm-2 in 2021. ② In terms of spatial characteristics, the concentrations of the three pollutants generally showed a pattern of high concentration in the north and low concentration in the south, with a high concentration in the north, uncharacteristic concentration in the middle, and low concentration in the south. ③ The sensitivity of O3 was as follows:η<2.3 in spring, which belonged to the VOCs control area; η<4.2 in summer, showing that most areas were NOx-VOCs coordinated control areas and a few areas were VOCs control areas; η<4.2 in autumn, which was primarily controlled by VOCs, with a few of them being NOx-VOC synergistic control areas; and η<2.3 in winter, which was a VOCs-controlled area. VOCs were primarily controlled in Shandong Province. ④ Owing to the high concentration of O3 in Shandong Province from 2005 to 2021, Jinan, the capital city of Shandong Province in 2021, was selected for ozone source analysis. The O3 concentration increased in Jinan in 2021 owing to two aspects. First, the long-distance air transportation primarily originated from Lianyungang City in Jiangsu Province and Cangzhou City in Hebei Province. Second, the close air mass transport originated from the pollution of cities near Jinan and the economic zones of the Yellow Sea and Bohai Sea, and the aggregation analysis results corresponded with those of the potential source contribution factor algorithm (PSCF) and the weighted trajectory analysis (CWT).

Assessment of Land Surface Temperature from the Indian Cities of Ranchi and Dhanbad during COVID-19 Lockdown: Implications on the Urban Climatology

An apparent increase in average global temperature is evident globally, and India is no exception. With the recent decade (2011–2020) arguably being the warmest, significant challenges due to rapid climate change have gained attention. However, notable spatial-temporal changes, especially with regard to land surface temperature (LST), were observed during the COVID-19 pandemic lockdown period, when a comparatively cooler climate was experienced in many urban centers. Assessment of LST, crucial in many heat-balance, land use, and climate change models research studies, depicts the near-surface hotness of the Earth’s temperature at a given location. Thus, this study utilizes satellite remote-sensing data to investigate the spatial-temporal variations of LST pre and post-lockdown imposed during the COVID-19 outbreak. Unlike many existing research studies on the metropolitans of India, the study considers developing Indian cities, Ranchi and Dhanbad, as its study area. Accurate LST computation was performed using existing LANDSAT-8 OLI/TIRS images and judged using other parameters (NDVI, LSE) obtained directly from the thermal infrared bands. The LST assessment successfully estimated temperature variations in Ranchi and Dhanbad, depicting a significant drop in temperature coinciding with the lockdown period and subsequent increase in urban temperature post-pandemic, indicating a meaningful relationship between human activities and urban surface temperature.

Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation

Forest fires are one of the most devastating natural disasters, and technologies based on remote sensing satellite data for fire prevention and control have developed rapidly in recent years. Early forest fire smoke in remote sensing images, on the other hand, is thin and tiny in area, making it difficult to detect. Satellites with high spatial resolution sensors can collect high-resolution photographs of smoke, however the impact of the satellite’s repeat access time to the same area means that forest fire smoke cannot be detected in time. Because of their low spatial resolution, photos taken by satellites with shorter return durations cannot capture small regions of smoke. This paper presents an early smoke detection method for forest fires that combines a super-resolution reconstruction network and a smoke segmentation network to address these issues. First, a high-resolution remote sensing multispectral picture dataset of forest fire smoke was created, which included diverse years, seasons, areas, and land coverings. The rebuilt high-resolution images were then obtained using a super-resolution reconstruction network. To eliminate data redundancy and enhance recognition accuracy, it was determined experimentally that the M11 band (2225–2275 nm) is more sensitive to perform smoke segmentation in VIIRS images. Furthermore, it has been demonstrated experimentally that improving the accuracy of reconstructed images is more effective than improving perceptual quality for smoke recognition. The final results of the super-resolution image segmentation experiment conducted in this paper show that the smoke segmentation results have a similarity coefficient of 0.742 to the segmentation results obtained using high-resolution satellite images, indicating that our method can effectively segment smoke pixels in low-resolution remote sensing images and provide early warning of forest fires.

Emergence and evolution of a new glacial lake during last climate normal by using remote sensing and GIS in Sikkim, India

Glaciers are important indicators of climate change. Melting of glaciers in the Himalayan region has resulted in formation of many glacial lakes. A newly formed lake was observed near the snout of the glacier. This study investigates evolution of this glacial lake using remote sensing satellite data from Landsat mission in last climate normal of around 30 years. Cloud-free data from years 1990, 1996, 2000, 2004, 2010, 2017, and 2021 are used for analysis. Normalized Differential Water Index and Normalized Differential Snow Index thresholding are applied to extract the surface area of the glacial lake and surface glacier cover. It was observed that during beginning of the last climate normal, lake area was 900 m2, and in 2021 it reached 51,300 m2. The clean glacial area decreased from 3,593,700 m2 in 1990 to 3,230,100 m2 in 2021. These changes are indicative of rapid deglaciation and an increase in debris cover as melting ice exposes and deposits debris previously embedded within the glacier. Land Surface Temperature (LST) change was analyzed using empirical equations to understand its relation with observed changes. It was observed that in last climate normal, the average LST was increased by +1.76 °C. The snout regions showed the highest LST change of +3.14 °C. Polynomial, spline and logarithmic regression was applied to access quality of results. A strong correlation between lake area and glacier area was observed with r2 of 0.93. An accuracy of 96.45% was achieved for the estimated changes of the lake with latest field-based result and 96.89% with latest Landsat image. This study successfully generated baseline data for the newly emerged East Rathong Glacial lake during the last climate normal, utilizing normalized differential index time series thresholding of glacier and lake, along with LST change detection.

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.

An Approach for Monitoring Shallow Surface Outcrop Mining Activities Based on Multisource Satellite Remote Sensing Data

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing.

On-farm soybean seed protein and oil prediction using satellite data

Soybean [Glycine max L. (Merr.)] seed composition is receiving increased attention among farmers, agronomists, and commodity traders. Increasing the ability to predict seed quality traits such as protein and oil at the field level before harvest will provide a competitive ability to segregate quality and create an economic advantage to position the production at both domestic and global markets. Therefore, this study aims to use remote sensing satellite data to spatially predict soybean seed protein and oil concentrations at the field level before harvest time. The dataset consisted of 47 fields located in Kansas and Iowa, United States, from the 2019 to 2021 seasons. Six machine-learning approaches (ElasticNet, Random Forest, XGBoost, LightGBM, CatBoost, and an ensemble) were tested evaluating different vegetation indices and spectral bands to predict before harvest seed protein and oil concentrations from satellite imagery. The optimal timing for training prediction models was identified within a week after the peak of the green chlorophyll vegetation index, with different spectral indices and bands of importance for each seed quality component. The XGBoost outperformed the rest of the algorithms for both seed quality traits. Overall, models reported an absolute error of 1.80 % for protein and 1.04 % for oil concentrations. Our research describes a pipeline that combines on-farm data, open access satellite imagery, an intensive use of spectral bands, and machine learning to forecast seed quality before harvest. Future research guiding crop management interventions should be directed to i) integrating major drivers of spatial variation of seed quality traits such as soil and weather data, and ii) exploring satellite data-fusion approaches and iii) assesing alternatives models such as deep learning methods.

Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data

The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb × Zn/Cu × Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces.

Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2, and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4-6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increased the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects, and landscape ecology uncertainty studies.

Improved ammonia emission inventory of fertilizer application for three major crops in China based on phenological data

NH3 has an important impact on atmospheric chemistry, and its reduction has become a potential pathway to alleviate haze pollution. The existing NH3 emission inventories still have significant uncertainties in terms of their temporal distributions. In this study, we combined satellite remote-sensing phenological data with ground-station phenological data to develop a method for the temporal allocation of NH3 emissions from fertilizer application. A high-resolution dataset for fertilizer application in China was established. We developed NH3 emission inventories for the fertilization of three major crops in China, with a resolution of 1/12° × 1/12°. The results showed that there was a significant temporal variation in fertilizer application dates across the country, mainly concentrated in June (17.16 %), July (19.08 %), and August (18.77 %). The majority of fertilizer application for the three major crops occurred during the spring and summer months, with a particular emphasis on April (5.72 Tg), May (7.05 Tg), and June (4.29 Tg). The total NH3 emission from the three major crops in China in 2019 was 2.73 Tg. The North China Plain (762.23 Gg) and Middle and Lower Yangtze River Plain (606.85 Gg) were identified as the primary regions for high NH3 emissions from fertilizer application. The results also showed that NH3 emissions from the three major crops were predominantly observed during summer, with a peak value in July (606.99 Gg), mainly because of the high proportion of topdressing fertilizers. Areas with high fertilizer application generally coincided with areas of high NH3 emissions. This study may be the first to utilize remote-sensing phenological data to establish the NH3 emission inventory, which is of great significance for further improving the accuracy of the NH3 emission inventory.

Analysis of the effect of the 2021 Semeru eruption on water vapor content and atmospheric particles using GNSS and remote sensing

Mount Semeru, an active volcano in East Java, Indonesia, erupted on December 4, 2021, following extreme rainfall that caused an avalanche of hot pyroclastic flows and lava. The tropospheric conditions and dominant particle components in the atmosphere can be monitored using Global Navigation Satellite System (GNSS) technology and remote sensing satellites. GNSS signal propagation delay in Precise Point Positioning (PPP) processing can be used to determine Zenith Tropospheric Delay (ZTD) and Precipitable Water Vapor (PWV) variables so that atmospheric conditions can be generated. In addition, by using remote sensing satellite data, it is possible to obtain rainfall data with high temporal resolution as well as the dominant particle and gas content values during eruptions. During the eruption period, the high value of PWV was dominated by the high intensity of precipitation during the rainy season. High rainfall before the eruption caused activity inside the mountain to increase, which occurred in avalanche type eruption. Apart from that, the atmosphere around Semeru was also dominated by SO2 content, which spreaded for tens of kilometers. SO2 content began to be detected significantly by remote sensing sensors on December 7, 2021. In this study, deformation and atmospheric monitoring were also carried out using low-cost GNSS at the Semeru Monitoring Station on September 9–15, 2022. The results of the ZTD and ZWD values show the dominance of the wet component, which is directly proportional to rainfall activity in this period.

Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin

Observations of kilometer-scale turbulent fluxes of sensible (H) and latent heat (LE) are required for the validation of flux estimate algorithms from satellite remote-sensing data and the development of parameterization schemes in the hydro-meteorological models. Since 2019, two sets of Optical and Microwave scintillometer (OMS) systems have been operated in the Heihe River Basin of northwestern China, one on an alpine grassland of upper reaches, another on an oasis cropland of middle reaches, to measure both the areal H and LE. Combined with the observations of eddy-covariance (EC) and meteorological tower systems in both sites, an improved procedure for OMS data processing is proposed. The newly proposed procedure especially improves the preprocessing of raw scintillation data, properly uses the current probably better Lüdi et al. (2005) method in deriving meteorological structure parameters, and chooses the coefficients of similarity functions by Kooijmans and Hartogensis (2016) in calculating fluxes. Evaluated with the results of rather homogeneous grassland, the area-averaged H and LE over the heterogeneous oasis are then determined. Estimates of H and LE agree reasonably well with those obtained from EC in most cases. However, the most interesting is that LE over the oasis during the early crop growing stages is clearly larger than that of EC; while both agree well during the longer crop grown periods. Footprint analysis shows that, compared with EC, the OMS has clearly larger source area that contains a slight area of orchard and shelterbelts distributed near the light path, leading to larger LE during the early stages of crop growth. The area-averaged evapotranspiration (ET) over the oasis is then analyzed more acceptably, which varies from 3 to 5 mm day−1 depending on meteorological conditions during the 39 days of the crop growing period. These results are used to validate the Penman-Monteith-Leuning Version 2 (PML-V2) scheme.

Optimized Artificial Neural Network for the Classification of Urban Environment Comfort using Landsat-8 Remote Sensing Data in Greater Jakarta Area, Indonesia

The development of computer vision technology as a type of artificial intelligence is increasing rapidly in various fields. This method uses deep learning methods based on artificial neural networks, a well-performed algorithm in multi-parameter analysis. One of the development of computer vision models and algorithms is for a thematic digital image classification, such as environmental analysis. Remote sensing based digital image classification is one of the reliable tools for environmental quality analysis. This study aims to perform neural network optimization for the analysis of the urban environment comfort based on satellite data. The input data used are 4 types of geobiophysical indexes as urban environmental comfort parameters derived from cloud-free annual mosaics Landsat-8 remote sensing satellite data. The results obtained in this study indicate that the 1 hidden layer neural network architecture with 16 neurons for the classification of urban environmental comfort and 10 other land cover classes is quite good. The result of the classification using this optimized artificial neural network shows that the distribution of classes is very uncomfortable which dominates the Greater Jakarta area and its surroundings. For other classes in the study area, some are uncomfortable and rather comfortable. By using this method, we obtained a fast classification training time of 18 seconds for 145 iterations to achieve an RMS Error of 0.01, and has a fairly high classification accuracy overall 89% with a Kappa coefficient of 0.88, while the 2 hidden layer neural network architecture does not succeed in achieving convergence

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.