Remote Sensing Data Sources Research Articles

A New Technique for Urban and Rural Settlement Boundary Extraction Based on Spectral–Topographic–Radar Polarization Features and Its Application in Xining, China

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

Fusion of Dense Airborne LiDAR and Multispectral Sentinel-2 and Pleiades Satellite Imagery for Mapping Riparian Forest Species Biodiversity at Tree Level.

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species.

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data.

Compound extreme inundation risk of coastal wetlands caused by climate change and anthropogenic activities in the Yellow River Delta, China

The coastal wetlands of the Yellow River Delta (YRD) in China are crucial for their valuable resources, environmental significance, and economic contributions. However, these wetlands are also vulnerable to the dual threats of climate change and human disturbances. Despite substantial attention to the historical shifts in YRD's coastal wetlands, uncertainties remain regarding their future trajectory in the face of compound risks from climate change and anthropogenic activities. Based on a range of remote sensing data sources, this study undertakes a comprehensive investigation into the evolution of YRD's coastal wetlands between 2000 and 2020. Subsequently, the potential fate of coastal wetlands is thoroughly analyzed through the Land Use/Cover Change (LUCC) simulation using System Dynamic-Future Land Use Simulation (SD-FLUS) model and the extreme water levels projection integrated future sea-level rise, storm surge, and astronomical high tide in 2030, 2050, and 2100 under scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results revealed that YRD's coastal wetlands underwent a marked reduction, shrinking by 1688.72 km2 from 2000 to 2020. This decline was mostly attributed to the substantial expansion in the areas of artificial wetlands (increasing by 823.78 km2), construction land (increasing by 767.71 km2), and shallow water (increasing by 274.58 km2). Looking ahead to 2030–2100, the fate of coastal wetlands appears to diverge based on different scenarios. Under the SSP1-2.6 scenario, the area of coastal wetland is projected to experience considerable growth. In contrast, the SSP5-8.5 scenario anticipates a notable decrease in coastal wetlands. Relative to the inundated area suffered from the current extreme water levels, the study projects a decrease of 6.8%–10.6% in submerged coastal wetlands by 2030 and 9.4%–18.2% by 2050 across all scenarios. In 2100, these percentages are projected to decrease by 0.4 % (SSP2-4.5) and 27.1% (SSP5-8.5), but increase by 35.7% (SSP1-2.6). Results suggest that coastal wetlands in the YRD will face a serious compound risk from climate change and intensified human activities in the future, with climate change being the dominant factor. More efficient and forward-looking measures must be implemented to prioritize the conservation and management of coastal wetland ecosystems to address the challenges, especially those posed by climate change.

Are raw satellite bands and machine learning all you need to retrieve actual evapotranspiration?

Accurately estimating latent heat flux (LE) is crucial for achieving efficiency in irrigation. It is a fundamental component in determining the actual evapotranspiration (ETa), which in turn, quantifies the amount of water lost that needs to be adequately compensated through irrigation. Empirical and physics-based models have extensive input data and site-specific limitations when estimating the LE. In contrast, the emergence of data-driven techniques combined with remote sensing has shown promising results for LE estimation with minimal and easy-to-obtain input data. This paper evaluates two machine learning-based approaches for estimating the LE. The first uses climate data, the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), while the second uses climate data combined with raw satellite bands. In-situ data were sourced from a flux station installed in our study area. The data include air temperatures (Ta), global solar radiation (Rg), and measured LE for the period 2015-2018. The study uses Landsat 8 as a remote sensing data source. At first, 12 raw available bands were downloaded. The LST is then derived from thermal bands using the Split Window algorithm (SW) and the NDVI from optical bands. During machine learning modeling, the CatBoost model is fed, trained, and evaluated using the two data combination approaches. Cross-validation of 3-folds gave an average RMSE of 27.54 W.nr2 using the first approach and 27.05 W.nr2 using the second approach. Results raise the question: Do we need additional computational layers when working with remote sensing products combined with machine learning? Future work is to generalize the approach and test it for other applications such as soil moisture retrieval, and yield prediction.

Study on the response of ecological sensitivity to land use and land cover changes in Jinzhai, China

Changes in land use types are often influenced by human activities, natural factors, and other factors. To explore the changes in land use types, in this paper, we focused on Jinzhai County in Anhui Province. Landsat series data from 2000 to 2020 were utilized as the primary remote sensing data source and combined with land use investigations, and the land use types were classified to better analyze their changes in Jinzhai County from 2000 to 2020. The main calculation indicators, such as the land use change rate and land use transfer matrix, were used to study the proportions of the different land use types during the past two decades, the comprehensive land use dynamics, and the directions of change of the different land use types.

A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA.

Study on the substitutability of nighttime light data for SDG indicators: a case study of Yunnan Province

Introdution: One crucial method to attain Sustainable Development Goals (SDGs) involves timely adjustment of development policies, promoting the realization of SDGs through a time-series assessment of the degree of accomplishment. In practical applications, data acquisition is a significant constraint in evaluating the SDGs, not only in China but across the globe. Hence, expanding data channels and exploring the feasibility of various data sources for sustainable development assessment are effective strategies to tackle the challenge of data acquisition.Methods: In light of this issue, this study selected Nighttime Light Data, a remote sensing data source closely linked to human social activities, as an alternative data source. Using Yunnan Province as an example, 16 localized indicators of social, economic, and environmental types were chosen. These indicators were then subjected to a correlation analysis with the Total Nighttime Light Index (TNLI). The relationships between different types of indicators and TNLI were analyzed at both temporal and spatial scales, thus identifying the indicators for which TNLI could serve as a suitable substitute measure.Results: The study indicates that when the SDG indicators are classified into economic, social and environmental categories, the total value of nighttime light presents a significant correlation and substitutability with economic indicators; significantly correlated with some social indicators, it can reveal the weak links in the development of underdeveloped areas; it is not significantly correlated with environmental indicators, while a trend correlation exists, which can provide some reference values.Discussion: This study has demonstrated the feasibility of using Nighttime Light Data for sustainable development assessment. It provides a novel evaluation method for countries that, despite a lack of resources for conducting sustainable development assessments, have a greater need for such assessments due to their lower economic development. Furthermore, a multitude of assessment methods can be developed based on Nighttime Light Data.

Detection of Ocean Internal Waves Based on Modified Deep Convolutional Generative Adversarial Network and WaveNet in Moderate Resolution Imaging Spectroradiometer Images

The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset’s size but also exhibit diverse characteristics, enhancing the model’s generalization performance. Furthermore, we have developed a deep neural network named “WaveNet,” which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet’s recognition rate.

A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points.

Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network

Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field.

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Universal Geographic Information System Productivity beneath optimum Underwater Sensor Networks Routing

Study Framework: The system of geographical information (GIS) is a computerized method for collecting, storing, disseminating, querying, updating, processing and analyzing geographic data. (GIS) enables the collection of diverse spatial data from various sources and transforms it into an information board for multiple users to boost productivity. Intelligent techniques, remote sensing, and underwater Wireless sensor networks are applied to improve GIS dramatically. Remote sensing data sources for GIS come from underwater sensor networks, The implementation of effective sensing devices, intelligent mobile nodes, and a multi-hop routing system can facilitate the efficient and optimal routing of traffic towards the basin or base station. They have been using GIS, which has revolutionized response scenarios with its ability to collect, analyze and manipulate spatial points. The objective of the study: The aim of this research is to differentiate the significant routing protocol issues that are relevant to underwater sensor networks. These networks serve as key transceivers that have established mechanisms for seamless and efficient data transmission. The mechanisms: The article presented a performance evaluation simulation based on a comparison of five different routing protocols that perform to promote GIS execution. A rigorous comparison of the outcomes varied by five routing protocols, where it evaluated with random mobility UnderWater sensor model based on the varied network parameters, was performed to validate this work. The continued to improve results: The suggested study in this manuscript contains spatial-information services universal to routing protocols to aid a group of navigators in harsh circumstances. According to the findings of the study, the CTP-ABS-SEEC routing protocol demonstrated superior performance compared to the other four routing protocols (CTP-SEEC, SEEC, EEDBR, and DBR) in the specified network configuration.

Estimation of Forest Stock Volume Using Sentinel-2 MSI, Landsat 8 OLI Imagery and Forest Inventory Data

Forest stock volume (FSV) is a key indicator for measuring forest quality, evaluating forest management capabilities, and the main factor for evaluating forest carbon sequestration levels. In this study, to achieve an accurate estimation of FSV, we used Ninth Beijing Forest Inventory data (FID), and Landsat 8 OLI and Sentinel-2 MSI imagery to establish FSV models. The performance of Landsat 8 and Sentinel-2 imagery data in estimating forest volume in Huairou District, Beijing, China was compared. The combination of Landsat 8 and Sentinel-2 satellite data was employed to create a new data source. Two variable selection methods, linear stepwise regression (LSR) and recursive feature elimination (RFE), were used to extract feature variables. The multiple linear regression(MLR) models, Back Propagation (BP) neural network models, and Random Forest (RF) models were employed to estimate forest volume in the study area based on the feature variables obtained from both data sources. The research results indicate (1) the Sentinel-2-based model achieved higher accuracy compared to the same model based on the Landsat 8 factor set. The correlation between the red-edge band of Sentinel-2 imagery and FSV is more significant than that of other characteristic variables used. Variables derived from the red-edge band have the potential to reduce model errors; (2) the estimation accuracy of the model can be significantly improved by using the RFE (Recursive Feature Elimination) method to select remote sensing feature variables. RFE is based on the importance ranking of all feature variables and selects the feature variables that contribute the most to the model. In the variable group selected by RFE, the texture features and the derived features from the red-edge band, such as SenB5, SenRVI, SenmNDVIre, and SenB5Mean, contribute the most to the improvement of model accuracy. Furthermore, in the optimal Landsat 8–Sentinel-2 RFE-RF model, where texture features are involved, the rRMSE is greatly reduced by 3.7% compared to the joint remote sensing RFE-RF model without texture features; (3) the MLR, BP, and RF models based on the modeling factor set established on Sentinel-2 have accuracy superior to the model accuracy established based on the modeling factor set of Landsat 8. Among them, the Random Forest (RF) method inverted by the recursive feature elimination (RFE) method using Sentinel-2A image has the best inversion accuracy effect (R2 = 0.831, RMSE = 12.604 m3 ha−1, rRMSE = 36.411%, MAE = 9.366 m3 ha−1). Comparing the performance of the models on the test set, the ranking is as follows, Random Forest (RF) model > Back Propagation (BP) neural network model > multiple linear regression (MLR) model. The feature variable screening based on the Random Forest’s recursive feature elimination (RFE) method is better than the linear stepwise regression (LSR). Therefore, the RFE-RF method based on the joint variables from Landsat 8 and Sentinel-2 satellite data to establish a new remote sensing data source provides the possibility to improve the estimation accuracy of FSV and provides reference for forest dynamic monitoring.

The uncertain geographic context problem (UGCoP) in measuring people’s exposure to green space using the integrated 3S approach

The “mobility turn” in environmental health studies promoted the integrated 3S (GIS, GPS, and remote sensing) approach in the study of urban residents’ exposure to green space and corresponding health outcomes. However, few studies have examined the uncertainty induced by contextual settings when measuring people’s exposure to green space using the conventional and integrated 3S approaches. In this paper, we compared the differences in green space exposure obtained from different geographic contexts using residence-based and mobility-based methods, multiple spatial resolutions, and buffer zones. We collected 7-day GPS trajectories from 208 participants at the 1-minute temporal resolution in Hong Kong. Entire Hong Kong’s green space was delineated using multiple remote sensing data sources at 3 m, 10 m, and 30 m spatial resolutions. Lastly, the residence-based and mobility-based measurements of exposure to green space were calculated for each participant using 100 m, 300 m, and 500 m buffer zones at three spatial resolutions. Descriptive analyses, t-tests, and logistic regression were employed to examine the influence of different contextual settings on different measurements of green space exposure and their health effects. The results indicate multiple significant disparities. The most striking difference is that mobility-based measurements of exposure to green space are significantly higher than those of residence-based measurements, manifesting the uncertain geographic context problem (UGCoP). For future studies, we suggest using mobility-based measurements of exposure to green space, smaller buffer zones, and finer spatial resolutions, which enable more accurate measurements of green space exposure for the study of green space’s health effects.

A Spatiotemporally Constrained Interpolation Method for Missing Pixel Values in the Suomi-NPP VIIRS Monthly Composite Images: Taking Shanghai as an Example

The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resulting in the absence of high-precision spatiotemporal continuous datasets. In this paper, we first select nine-time series interpolation methods to interpolate the missing images. Second, we construct image pixel-level temporal continuity constraints and spatial correlation constraints and remove the pixels that do not meet the constraints, and the eliminated pixels are filled with the focal statistics tool. Finally, the accuracy of the time series interpolation method and the spatiotemporally constrained interpolation method (STCIM) proposed in this paper are evaluated from three aspects: the number of abnormal pixels (NP), the total light brightness value (TDN), and the absolute value of the difference (ADN). The results show that the images simulated by the STCIM are more accurate than the nine selected time series interpolation methods, and the image interpolation accuracy is significantly improved. Relevant research results have improved the quality of the VIIRS dataset, promoted the application research based on the VIIRS DNB long-time series night light remote sensing image, and enriched the night light remote sensing theory and method system.

Analysis of Spatio-Temporal Evolution of Regional Settlements under the Background of Rapid Urbanization: A Case Study in Sishui County, China

China’s urbanization has achieved rapid development in the past 20 years, with towns expanding in size and the population increasing, while rural society has also undergone dramatic changes. An in-depth study on the evolution process of rural settlements in the context of rapid urbanization is beneficial to the rational planning of villages and the promotion of green and sustainable urban development. Located in East China, Sishui County is in the transition area between three types of landforms: hills, plains, and mountains. The spatial distribution of rural settlements in the urbanization process shows obvious regional differences. To our best knowledge, research on the spatio-temporal evolution of regional settlements in Sishui County is rare. In this study, we chose Sishui County as the study area, utilized Landsat5 (TM) and Landsat8 (OLI) satellite data as remote sensing data sources, and applied Geographic Information System (GIS) spatial analysis methods, central place theory, and core–periphery theory to explore the evolution process for the pattern, scale, and structure of rural settlements in this region from 2000 to 2021 and to investigate the influencing factors. The results show that: (1) in terms of the evolution of the rural settlement pattern, its distribution shows a gradual increase in the degree of dispersion, which indicates an overall development trend in the region of Sishui County in recent years and that the trend is gradually increasing; (2) in terms of scale evolution, the degree and speed of expansion in rural settlements of Sishui County have gradually decreased, and the scale grade has gradually increased; (3) in terms of structural evolution, the hierarchy system in rural settlements of Sishui County is constantly being improved and optimized from a simple to a complex core–periphery structure. These results will provide data support for the rational planning of villages and sustainable, high-quality urban development. They will also help local governments take appropriate measures to achieve coordinated and sustainable socio-economic and environmental development in the region.

Classification of Land Cover in Complex Terrain Using Gaofen-3 SAR Ascending and Descending Orbit Data

Synthetic aperture radar (SAR) image is an effective remote sensing data source for geographic surveys. However, accurate land cover mapping based on SAR image in areas of complex terrain has become a challenge due to serious geometric distortions and the inadequate separation ability of dual-polarization data. To address these issues, a new land cover mapping framework which is suitable for complex terrain is proposed based on Gaofen-3 data of ascending and descending orbits. Firstly, the geometric distortion area is determined according to the local incident angle, based on analysis of the SAR imaging mechanism, and the correct polarization information of the opposite track is used to compensate for the geometric distortion area, including layovers and shadows. Then, the dual orbital polarization characteristics (DOPC) and dual polarization radar vegetation index (DpRVI) of dual-pol SAR data are extracted, and the optimal feature combination is found by means of Jeffries–Matusita (J-M) distance analysis. Finally, the deep learning method 2D convolutional neural network (2D-CNN) is applied to classify the compensated images. The proposed method was applied to a mountainous region of the Danjiangkou ecological protection area in China. The accuracy and reliability of the method were experimentally compared using the uncompensated images and the images without DpRVI. Quantitative evaluation revealed that the proposed method achieved better performance in complex terrain areas, with an overall accuracy (OA) score of 0.93, and a Kappa coefficient score of 0.92. Compared with the uncompensated image, OA increased by 5% and Kappa increased by 6%. Compared with the images without DpRVI, OA increased by 4% and Kappa increased by 5%. In summary, the results demonstrate the importance of ascending and descending orbit data to compensate geometric distortion and reveal the effectiveness of optimal feature combination including DpRVI. Its simple and effective polarization information compensation capability can broaden the promising application prospects of SAR images.

Large-scale agricultural burning and cardiorespiratory emergency department visits in the U.S. state of Kansas.

Prescribed agricultural burning is a common land management practice, but little is known about the health effects from the resulting smoke exposure. To examine the association between smoke from prescribed burning and cardiorespiratory outcomes in the U.S. state of Kansas. We analyzed a zip code-level, daily time series of primary cardiorespiratory emergency department (ED) visits for February-May (months when prescribed burning is common in Kansas) in the years 2009-2011 (n = 109,220). Given limited monitoring data, we formulated a measure of smoke exposure using non-traditional datasets, including fire radiative power and locational attributes from remote sensing data sources. We then assigned a population-weighted potential smoke impact factor (PSIF) to each zip code, based on fire intensity, smoke transport, and fire proximity. We used Poisson generalized linear models to estimate the association between PSIF on the same day and in the past 3 days and asthma, respiratory including asthma, and cardiovascular ED visits. During the study period, prescribed burning took place on approximately 8 million acres in Kansas. Same-day PSIF was associated with a 7% increase in the rate of asthma ED visits when adjusting for month, year, zip code, meteorology, day of week, holidays, and correlation within zip codes (rate ratio [RR]: 1.07; 95% confidence interval [CI]: 1.01, 1.13). Same-day PSIF was not associated with a combined outcome of respiratory ED visits (RR [95% CI]: 0.99 [0.97, 1.02]), or cardiovascular ED visits (RR [95% CI]: 1.01 [0.98, 1.04]). There was no consistent association between PSIF during the past 3 days and any of the outcomes. These results suggest an association between smoke exposure and asthma ED visits on the same day. Elucidating these associations will help guide public health programs that address population-level exposure to smoke from prescribed burning.

The use of remote sensing data sources and GIS in fire protection planning in biebrza national Park, with special attention to non-forest ecosystems

ABSTRACT In the spring of 2020, a large-scale fire destroyed 5,526 hectares of wetlands in Biebrza National Park. It was decided to modernize the park’s fire protection system. It proved necessary to develop rules for fire protection in large natural areas where non-forest communities predominate. A team of experts from the Forest Research Institute, the Fire Department, and the Biebrza National Park developed methods for fire protection in the park based on open, publicly available remote sensing data sources. The following methods were developed or modified and adapted to the conditions of the park: determination of fire danger, prediction of fire danger, modeling of fire development, determination of the network of fire access roads, determination of water supply points, including water withdrawal points for helicopters, detection of fires, functioning of the alarm and communication system, firebreaks, places of concentration of forces and resources, equipment for fire fighting, and taking preventive measures. The developed principles of fire protection in the Biebrza National Park, based on open remote sensing data and the technology of GIS, will enable the creation and operation of a system that will reduce the probability of fires in the future.