Remote Sensing Research Articles

Machine Learning and Remote Sensing Applications for Assessing Land Use and Land Cover Changes for Under-monitored Basin

Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals.

Meta-heuristics time series prediction with Reduced Noise and Time Series Disintegration in Remote Sensing

Meta-heuristics time series prediction with Reduced Noise and Time Series Disintegration in Remote Sensing

Remote sensing and feminist critique: Reappropriations of sensing across distance

Remote sensing technologies and Geographic Information Systems (GIS) have sparked diverse conceptual and critical responses, with feminist approaches in particular undergoing notable transformations. This article traces and contextualizes these changes, emphasizing the re-evaluation of the concept of “distance” in feminist critiques of remote sensing (RS) and GIS from the 1990s onwards. It highlights a shift from outright rejection of GIS/RS technologies as tools of masculinist, positivistic science to a redefinition of remotely sensed information and imbricated understandings of distance and proximity. The article argues that researchers view issues of masculinism from markedly different perspectives and re-evaluate the role of technology-mediated seeing and imagining, resulting in a re-evaluation of physical distance in its relationship to infrastructure, access, and care. What has transformed is what it means to be “critical” toward as well as the possibility to be “critical” with remote sensing from a feminist point of view.

Edge-Cloud Remote Sensing Data-Based Plant Disease Detection Using Deep Neural Networks with Transfer Learning.

The identification and control of plant diseases have become increasingly complex as agricultural lands expand globally. Traditional manual methods of monitoring and diagnosing diseases are not feasible for large-scale farms. The complexity is further heightened by the heterogeneity of data types collected through remote sensing methods, such as images, videos, and sensor readings, which need to be processed in real-time. This paper introduces a novel EdgeCloud Remote Sensing architecture that utilizes Deep Neural Networks (DNNs) with Transfer Learning to enhance the detection of plant diseases using remote sensing data. The proposed system employs a Fuzzy Deep Convolutional Neural Network (FCDCNN) to process multimodal data collected from both edge nodes and satellite point clouds. Transfer learning allows the sharing of model weights between cloud and edge nodes, thus optimizing performance without requiring frequent retraining. Simulations show that the proposed system achieves a 98% detection accuracy and reduces processing time by 25% compared to existing methods. By distributing tasks between edge and cloud resources, the system is able to process large datasets effectively and improve disease detection performance on vast farmlands.

SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling

Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas.

Multi-Scale and Multi-Network Deep Feature Fusion for Discriminative Scene Classification of High-Resolution Remote Sensing Images

The advancement in satellite image sensors has enabled the acquisition of high-resolution remote sensing (HRRS) images. However, interpreting these images accurately and obtaining the computational power needed to do so is challenging due to the complexity involved. This manuscript proposed a multi-stream convolutional neural network (CNN) fusion framework that involves multi-scale and multi-CNN integration for HRRS image recognition. The pre-trained CNNs were used to learn and extract semantic features from multi-scale HRRS images. Feature extraction using pre-trained CNNs is more efficient than training a CNN from scratch or fine-tuning a CNN. Discriminative canonical correlation analysis (DCCA) was used to fuse deep features extracted across CNNs and image scales. DCCA reduced the dimension of the features extracted from CNNs while providing a discriminative representation by maximizing the within-class correlation and minimizing the between-class correlation. The proposed model has been evaluated on NWPU-RESISC45 and UC Merced datasets. The accuracy associated with DCCA was 10% and 6% higher than discriminant correlation analysis (DCA) in the NWPU-RESISC45 and UC Merced datasets. The advantage of DCCA was better demonstrated in the NWPU-RESISC45 dataset due to the incorporation of richer within-class variability in this dataset. While both DCA and DCCA minimize between-class correlation, only DCCA maximizes the within-class correlation and, therefore, attains better accuracy. The proposed framework achieved higher accuracy than all state-of-the-art frameworks involving unsupervised learning and pre-trained CNNs and 2–3% higher than the majority of fine-tuned CNNs. The proposed framework offers computational time advantages, requiring only 13 s for training in NWPU-RESISC45, compared to a day for fine-tuning the existing CNNs. Thus, the proposed framework achieves a favourable balance between efficiency and accuracy in HRRS image recognition.

Advanced Remote Sensing and Generative Models for Comprehensive Water Quality Management in a Changing Climate

Advanced Remote Sensing and Generative Models for Comprehensive Water Quality Management in a Changing Climate

High-Performance Processing Electronics for Cooler Temperature Sensors in Infrared Payloads

Remote Sensing Infra-Red (IR) imaging sensors operate at very low temperatures (40–100 K). Generally, these types of imaging sensors are integrated with cryo-coolers. These sensors have an inbuilt 2N2222 diode-based cooler temperature sensor (CTS). This paper presents details of low-noise, high=precision processing electronics developed for CTS. Critical design aspects, simulation and hardware results of the system are discussed in this paper. Developed electronics systems achieve ≈5mK resolution and the power dissipation recorded is < 200 mW.

Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces

Ecological Environmental Quality (EEQ) assessment is a crucial method for understanding regional ecological conditions and identifying ecological problems. Understanding how EEQ responds to various natural and anthropogenic factors under different landform conditions is extremely important to improve the accuracy and usefulness of the assessment. Landforms, as a key natural geographic element, influence not only the spatial distribution of EEQ, but also the nature and intensity of human activities. However, we lack a comprehensive understanding of how different landform types affect the ecological environment. In this paper, we quantitatively assessed the spatial heterogeneity characteristics of EEQ in Myanmar based on the Remote Sensing Ecological Index (RSEI) model, and used Geodetector to reveal the driving mechanisms of EEQ in terms of the whole region and specific landform types. The results showed that: (1) The overall EEQ in Myanmar showed a fluctuating downward trend from 2000 to 2020, and the spatial heterogeneity is obvious. The plains and hills had poor EEQ, while the mountains, highlands and most of the northern regions have better EEQ. (2) The EEQ of different landform types differed significantly. The EEQ was best in middle relief mountains and poorer in plains and high relief mountains. (3) Land use, human footprint and net primary productivity (NPP) were the main factors affecting EEQ in Myanmar, and there are significant spatial and temporal dynamic interactions between different factors. (4) There were significant differences in the factors affecting EEQ between landform types. For example, EEQ in plains, hills, low relief mountains, and middle relief mountains was influenced by a combination of natural and anthropogenic factors. In contrast, upland mountain areas were mainly influenced by natural factors alone. As the degree of landform fluctuation increases, the influence of each factor generally showed a downward trend. This study reveals the spatio-temporal variation characteristics of Myanmar’s EEQ, key driving factors, and differential response mechanisms of ecological environment evolution under different landform types, and provides a scientific basis for formulating ecosystem protection and restoration strategies based on local conditions.

Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)

Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.

Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies.

YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

Investigating the Role of Cover-Crop Spectra for Vineyard Monitoring from Airborne and Spaceborne Remote Sensing

The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-cropuav and pure vineuav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-cropuav was a successful predictor of grape yield and quality parameters (range of R2 = 0.37–0.45), with model evaluation results comparable to pure vineuav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-cropuav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management.

Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images

Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods.

Extraction of Canal Distribution Information Based on UAV Remote Sensing System and Object-Oriented Method

At present, the extraction of irrigation canal network distribution information is of great significance for developing a digital twin irrigation district. However, due to the low resolution of remote sensing images, it is difficult to effectively identify the canal networks, especially for channels with a width of less than 1 m, where recognition is insufficient. Therefore, the purpose of this study is to extract canal networks of different widths in an irrigation district in Shaanxi Province as the research area. A rule-based object-oriented classification method was employed, utilizing image data collected by the DJI Mavic 3 multispectral UAV (Unmanned Aerial Vehicle) to explore the accuracy of this method in extracting canal distribution information. Based on UAV multispectral remote sensing imagery, the segmentation parameters for the remote sensing imagery were determined using ENVI 5.6 software, with the segmentation threshold set at 60 and the merging threshold set at 80. By combining the spectral and spatial differences between the canals and other ground objects, rules for extracting canal network distribution information were established, and the information on the distribution of channels in this irrigation area was finally obtained. The experimental results showed a maximum recall rate of 91.88% and a maximum precision rate of 57.59%. The overall recall precision rates for the irrigation district were 85.74% and 55.08%, respectively. This method provides a new solution for identifying and extracting canal systems in irrigation districts, offering valuable insights for acquiring canal distribution information and providing a scientific basis for precision irrigation.

Remote Sensing Evaluation of Trophic Status in the Daihai Lake Based on Fuzzy Classification

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R2 = 0.80, R2 = 0.83, and R2 = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R2 values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R2 of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R2 of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment.

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.

Software Application for Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms (SMASH): A Tool for Identifying Cyanobacteria Genera from Remotely Sensed Data

Software Application for Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms (SMASH): A Tool for Identifying Cyanobacteria Genera from Remotely Sensed Data

Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data

High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area.