Pixel-based Image Research Articles

Mapping Sea Grass Coverage of Tanjung Benoa Bali Using Medium Resolution Satellite Imagery Sentinel 2B

Seagrass beds are important components of a coastal ecosystem. This ecosystem serves as the primer producers of the water food chain, habitat for marine biota, produces organic carbon, and indirectly contributes to the economic well-being of coastal communities. However, the ecosystem is vulnerable to damage caused by natural factors and human activities. The objectives of this study were, firstly to identify the distribution of seagrass beds in Tanjung Benoa using Sentinel 2B satellite imagery and secondly to compare classification results from two different approaches namely pixel-based image classification and object-based image classification. Accuracy-test was carried out using field data reference of 195 sample points in the form of a 10 m X 10 m transect. The image pre-processing process was conducted with Bottom of Atmosphere (BoA) correction using the Dark Object Subtraction (DOS) method. Furthermore, the water column correction was performed using the Depth Invariant Index (DII) and the Lyzenga algorithm. The mapping results showed that the area of seagrass beds in the shallow waters of Tanjung Benoa reaches 242.99 ha. There were seven seagrass species in the study area, with an average cover of 75%. The accuracy of object-based image classification was higher than that of pixel-based classification with a difference up to 25% for six classes classification and 15% for two classes classification. Excellent results for classifying seagrasses based on cover density can be obtained when high-resolution satellite imagery and OBIA are combined with the SVM or Fuzzy Logic algorithm.

Void fraction research and destructive examination of CFETR toroidal field coil protype joints

The CFETR toroidal field coil (TFC) is designed to generate a peak magnetic field of 14.5 T at 4.2 K, with an operating current of 95.6 kA. The creation of superconducting joints is one of the most delicate parts of TF winding package (WP) fabrication, as the superconducting joints play an important role in the electrical and cooling circuit connection. ASIPP has produced a prototype sample containing three types of joints using a CFETR TF cable in conduit conductor (CICC) in 2022 and has obtained good resistance test values. This paper describes the study of joint void fraction (VF) and pixel-based image recognition analysis, and evaluation of contacts between conductive components. And the solution of the expansion of the twin box terminal in the manufacturing process and the groove design examination of spliced joints.

An adaptive beamformer based on dynamic phase coherence factor for pixel-based medical ultrasound imaging.

Pixel-based beamforming realizes dynamic focusing at the pixel level with a focused beam by assuming that the received signals are composed of spherical pulses. Far-focused pixel-based (FPB) imaging was proposed to avoid artifacts around the focal depth. However, the contrast improvement is limited. We propose an adaptive weighting method based on dynamic phase coherence factor (DPCF) to improve the image contrast while preserving the speckle pattern. The phase variation is dynamically estimated based on the noise energy proportion of echo signals and it is used to calculate phase coherence weights for suppressing interference and preserving desired signals. A depth-dependent parameter is designed for DPCF to enhance the performance of noise and clutter suppression in the far-field region. We further use the subarray averaging technique to smooth the speckle texture. The proposed method was evaluated on simulated, phantom experimental, and in vivo data. Results show that, compared with the phase coherence factor (PCF) based method, DPCF respectively leads to average CR improvements by more than 60% and 24% in simulation and experiment, while obtaining an improved speckle signal-to-noise ratio. The proposed method is a potentially valuable approach to obtaining high-quality ultrasound images in clinical applications.

Spatial prediction of the urban inter-annual land surface temperature variability: An integrated modeling approach in a rapidly urbanizing semi-arid region

The inter-annual land surface temperature (LST) is a meteorological indicator of urban environments, affecting energy consumption and quality of life. In this study, the annual LST variability (ALSTV) of a rapidly urbanizing region in Iran including Karaj, Shahriar and Mohammad-Shahr cities and their surrounding suburbs was estimated using Landsat-8 images. Pixel-based image classification and object-based segmentation techniques were employed to extract built-up patches and spectrally homogeneous regions. Using the multiple regression analysis, the ALSTV of built-up patches was modeled as a function of their structure and the spatial characteristics of other land-use patches located in their respective segments (as neighborhood factors). A Cellular Automata model was utilized to simulate the expansion of built-up area up to 5% and, therefore, estimate their future ALSTV. The mean ALSTV was highest in urban areas (33.25± 7.41 °C), while the lowest mean value of 24.12± 3.24 °C was in green covers. The ALSTV of built-up patches were positively associated with their area and the percentage and size of neighborhood built-up patches, while negatively with the percentage of neighborhood green patches. To create a thermally comfortable landscape, additional green patch allocation and preventing excessive urban patch growth are required in all large built-up segments.

SO SÁNH CÁC PHƯƠNG PHÁP PHÂN LOẠI ẢNH VỆ TINH LANDSAT-8 ĐỂ XÂY DỰNG BẢN ĐỒ LỚP PHỦ MẶT ĐẤT TẠI THÀNH PHỐ HẠ LONG, TỈNH QUẢNG NINH

For a long time, many pixel-based image classification algorithms have been developed for identifying land cover, some of which are commonly used due to their efficiency and accuracy such as Maximum Likelihood (MLC), Support Vector Machines (SVMs), and Decision Trees (DTs). These methods are applied to classify land cover in Ha Long city using Landsat-8 satellite images with several categories including residence, bare soil, forest, agricultural land, water surface, and coal field. The validation results show that the overall accuracy (OA) and Kappa coefficient (K) of these classification methods are high, with OA > 91 % and K > 0.9. However, compared to the other two methods, the DTs method provides the results with the highest accuracy and the best ability to separate features. The obtained results allow selecting the image classification method for identical areas with complicated land cover such as Ha Long.

Identification of Streamside Landslides with the Use of Unmanned Aerial Vehicles (UAVs) in Greece, Romania, and Turkey

The alleviation of landslide impacts is a priority since they have the potential to cause significant economic damage as well as the loss of human life. Mitigation can be achieved effectively by using warning systems and preventive measures. The development of improved methodologies for the analysis and understanding of landslides is at the forefront of this scientific field. Identifying effective monitoring techniques (accurate, fast, and low cost) is the pursued objective. Geographic Information Systems (GISs) and remote sensing techniques are utilized in order to achieve this goal. In this study, four methodological approaches (manual landslide delineation, a segmentation process, and two mapping models, specifically object-based image analysis and pixel-based image analysis (OBIA and PBIA)) were proposed and tested with the use of Unmanned Aerial Vehicles (UAVs) and data analysis methods to showcase the state and evolution of landslides. The digital surface model (DSM)-based classification approach was also used to support the aforementioned approaches. This study focused on streamside landslides at research sites in three different countries: Greece, Romania, and Turkey. The results highlight that the areas of the OBIA-based classifications were the most similar (98%) to our control (manual) classifications for all three sites. The landslides’ perimeters at the Lefkothea and Chirlesti sites showed similar results to the OBIA-based classification (93%), as opposed to the Sirtoba site, where the perimeters of the landslides from OBIA-based classification were not well corroborated by the perimeters in the manual classification. Deposition areas that extend beyond the trees were revealed by the DSM-based classification. The results are encouraging because the methodology can be used to monitor landslide evolution with accuracy and high performance in different regions. Specifically, terrains that are difficult to access can be surveyed by UAVs because of their ability to take aerial images. The obtained results provide a framework for the unitary analysis of landslides using modern techniques and tools.

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

Freshwater aquaculture in Jianghan Plain plays a key role in the whole industry of China. It was expanding rapidly to meet the fast-growing demand of consumption in these decades. The spatial distribution change of aquaculture in Jianghan Plain has attracted many researchers in recent years and it is worth further investigating. However, the accuracy and the quality of inland aquaculture classification and mapping still have space to be improved. Our study attempts to use Sentinel-1 and Sentinel-2 data to classify aquaculture areas, non-aquaculture water, and non-water areas. We applied image segmentation and pixel-based feature computation to obtain candidate datasets. Recursive feature elimination (RFE) was used to find the optimal datasets which were then imported into a modified UNet for classification. This workflow was then applied to the annual datasets for better observation of the spatial change of aquaculture areas. In total 12 indices and features were found most influential and preserved after RFE. The overall classification accuracy reached 96.83%, while the precision and recall of aquaculture areas reached 84.47% and 90.48%, respectively. The combination of object-based and pixel-based image analysis showed its advantage in inland aquaculture classification compared to other studies. Integrated rice–crawfish farming was found a key factor motivating the rapid development of aquaculture. Policies about the fishing ban and ecosystem restoration were also found deeply affecting the spatial change of aquaculture in Jianghan Plain.

Mechanical Performances and Frost Resistance of Alkali-Activated Coal Gangue Cementitious Materials

The coal gangue after composite activation treatment is considered a potential low-carbon and green cementitious material, so the feasibility of employing composite-activated gangue to partially or entirely replace cement for building materials is systematically studied in this paper. The effects of alkali content, slag content, and water-to-binder ratio on the mechanical properties and frost resistance of alkali-activated coal gangue mortar (AACGM) were experimentally investigated. An ESEM was employed to observe the microstructure of the AACGM. Moreover, the microstructure damage to the AACGM was analyzed by a pixel-based image processing algorithm. The research was carried out in accordance with standards JGJ/T70-2009 and GB/T 50082-2009. Experimental results indicated that the mechanical properties and frost resistance of the AACGM were superior to those of ordinary Portland cement mortar (OPCM). Compared with the OPC group, the compressive and flexural strength of the W0.50 group increased by 16.01% and 14.19%. Moreover, the loss rate of mass, flexural strength, compressive strength, and microstructure damage of the AACGM were less than those of the OPCM. Between freeze–thaw cycles 25 and 100, the cracks and pores of specimens gradually grew, and the maximum crack width increased by 277.78%. In addition, the slag was beneficial in improving the flexural strength, compressive strength, and frost resistance of the AACGM. Finally, the freeze–thaw damage mechanism of the CGBG mortar was systematically analyzed.

Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d’Ivoire

Forest monitoring requires more automated systems to analyze high ecosystem heterogeneity. The traditional pixel-based detection method has proven to be less and less effective. A novel change detection method is therefore proposed to detect changes in forest cover using satellite images at very high spatial resolution. This is object-oriented classification, which groups pixels into interpreted objects, based on their spectral values, spatial, and textural properties. Using sentinel and Lansat images, we tested for the first time in the West African rainforest zone the effectiveness of this method for better detection, delineation, and analysis of land use and occupation types. The mean shift algorithm was used in both the segmentation and classification processes. Next, we compared the proposed object-oriented method with a pixel-based image classification detection method by implementing both methods under the same conditions. High detection accuracy (> 90%) and an overall Kappa greater than 0.90 were obtained by the object-oriented method, which is about 20% higher than the pixel-based method. The object-based method was free of salt and pepper effects and was less prone to image misregistration in terms of change detection accuracy and mapping results. This study demonstrates that the object-based classifier is a much better approach than the classical pixel-based classifier. In addition, it shows the problems of detecting heterogeneous landscapes and explains the observed confusions between the types of vegetation formations specific to tropical wetlands. The results obtained are encouraging and the contribution of high-resolution images and the object-based method to better discrimination of tropical wetland vegetation is discussed.

Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image

Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective.

Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in 'Madoka' Peaches.

Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.

Deep learning for land use and land cover classification from the Ecuadorian Paramo.

ABSTRACT The paramo, plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon. This research was carried out in the province of Tungurahua, specifically the Quero district. The aim is to develop a classification of the land use land cover (LULC) in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance, for which three different approaches were applied: Pixel-Based Image Analysis (PBIA), Geographic Object-Based Image Analysis (GEOBIA), and a Deep Neural Network (DNN). Various parameters were used, such as the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI), texture, altitude, and slope. Seven classes were used: paramo, pasture, crops, herbaceous vegetation, urban, shrubrainland, and forestry plantations. The data was obtained with the help of onsite technical experts, using geo-referencing and reference maps. Among the models used the highest-ranked was DNN with an overall precision of 87.43%, while for the paramo class specifically, GEOBIA reached a precision of 95%.

Using sentinel and comparing two classification algorithms for land cover mapping in the area dominated by small scale heterogeneous agricultural land

Sentinel images are widely used for monitoring and mapping our environment phenomenon. The imagery applies in land-use and land cover mapping using pixel-based classification, image segmentation, or other image interpretation algorithms. One type of algorithm may be more suitable for a specific area, depending on many factors. This study aims to analyse and compare two classification algorithms for land cover (LC) mapping in the region characterised by a small scale type of agricultural land occupation. The primary input for this study is the Sentinel 2A image. Two well-known pixel-based classification algorithms, i.e., Maximum Likelihood classifier (MLC) and ECHO (Extraction and Classification of Homogeneous Objects), are used and are compared. The study covers an area of 3320.3 km2. The classification result produces nine (9) land cover classes, i.e., (1) pavement or urban area, (2) heterogeneous agricultural land, (3) irrigated paddy, (4) open water body, (5) dense vegetation or forest, (6) sparse vegetation or plantation, (7) shrubland or dry-land, (8) wetlands, and (9) sand-clay-rock. Classification using MLC and ECHO produced kappa and overall accuracies of more than 90%. In general, both algorithms can produce a relatively similar area extend for each class. However, two classes, i.e., (2) heterogeneous agricultural land and (6) sparse vegetation, are still tricky to distinguish.

EVALUATING PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF OPTICAL, SAR, AND AN EXTREME GRADIENT BOOSTING MODEL

Abstract. The above-ground biomass (AGB) estimation monitoring provides a powerful tool for the assessment of carbon emission and sequestration. Using remote sensing technique is an environmentally friendly way of biomass estimation. Thus, this paper investigated optical (i.e. Landsat 8 OLI and Sentinel-2), synthetic aperture radar (SAR) (global phased array type L-band SAR (PALSAR/PALSAR-2) and Sentinel-1), and their integration for AGB estimation of the Pack demonstration forest in Warrensburg, NY. Importantly, a LiDAR AGB raster of the study area was used as reference data for training/testing purposes. Then, an extreme gradient boosting (Xgboost) machine learning model was used to predict biomass values. The major goal of this study was to compare the performance of pixel-based and object-based image analysis (OBIA) for the AGB estimation. Results indicated that the object-based approach improved the RMSE of AGB prediction about 6.28 Mg/ha for optical + SAR, 6.17 Mg/ha for SAR, and 5.6 Mg/ha for optical data in comparison to the pixel-based approach. Moreover, the combination of optical and SAR data increased the prediction accuracy regardless of feature extraction approach.

Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine

Accurate distribution of irrigated, rainfed and paddy croplands is essential for food production and agricultural management. High spatial resolution land cover datasets rarely have detailed information about irrigated or rainfed croplands, while cropland datasets labelled with crop watering methods face challenges with coarse temporal and spatial resolution. This study proposed a semi-automatic detailed croplands mapping framework by integrating pixel-based classification and image segmentation. First, high-quality Sentinel-2 images were selected and mosaicked to time-series image dataset using Google Earth Engine platform. Second, pixel-based random forest classification and the Simple Non-Iterative Clustering image segmentation were integrated by optimal rules for generating 10-m resolution Detailed Croplands Map (DCM-2020). The DCM-2020 was assessed by using validation samples, and the overall accuracy of the map was 88.6% with kappa coefficient of 0.83. Compared with the existing cropland datasets, DCM-2020 shows much finer details, and croplands area is closer to official statistics.

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis

Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model.

Identification and Counting of European Souslik Burrows from UAV Images by Pixel-Based Image Analysis and Random Forest Classification: A Simple, Semi-Automated, yet Accurate Method for Estimating Population Size

Burrowing mammals such as European sousliks are widespread and contribute significantly to soil ecosystem services. However, they have declined across their range and the non-invasive estimation of their actual population size has remained a challenge. Results support that the number of burrow entrances is positively correlated with population abundance, and burrow locations indicate the occupied area. We present an imagery-based method to identify and count animals’ burrows semi-automatically by combining remotely recorded red, green, and blue (RGB) images, pixel-based imagery, and random forest (RF) classification. Field images were collected for four colonies, then combined and processed by histogram matching and spectral band normalization to improve the spectral distinctions among the categories BURROW, SOIL, TREE, and GRASS. The accuracy indexes of classification for BURROW kappa (κ) were 95% (precision) and 90% (sensitivity). A 10-iteration bootstrapping of the final model resulted in coefficients of variation (CV%) of BURROW κ for sensitivity and precision lower than 5%; moreover, CV% values were not significantly different between those scores. The consistency of classification and balanced precision and sensitivity confirmed the applicability of this approach. Our approach provides an accurate, user-friendly, and relatively simple approach to count the number of burrow openings, estimate population abundance, and delineate the areas of occupancy non-invasively.

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

CONTRIBUTION OF REMOTE SENSING TO ONION AGRICULTURAL SYSTEM IMPROVEMENT IN GUIDIMOUNI BASIN (ZINDER REGION)

New information and earth observation technologies, remote sensing and the Geographic Information System (T-GIS), have become very effective tools in crop mapping for better management of agricultural plots. The objective of this study is to propose a combination of spectral indexes (SI) based on a time series of sentinel-2 images by comparing the performance of three (3) classification algorithms, namely: Wide Margin Separators (WMS), the Random Forest (RF) and the Decision Trees (DT) in order to produce a map of land cover (LC) in the gardening sites of Guidimouni by analysis of pixel-based images. Five classes of land cover have been retained, namely: Onion, Other plants, Flood zones, Water, and Non-vegetation. 24 optical images were processed to draw the temporal curves of cultures that will be used for processing. 51 classification schemes were tested and evaluated. Thus, the different values of the NDVI time series made it possible to observe three stages linked to the cultural development of onion, namely stage-1 (beginning of germination and the appearance of the first leaves), stage-2 (early bulb formation and strong chlorophyll activity) and stage-3 (bulb thickening and leaf yellowing). On the other hand, the WMS classifiers made it possible to obtain a better mapping of land cover in terms of discrimination between classes by combining the five spectral indexes (NDVI, SAVI, EVI, IV, BI2) with a cartographic precision of 88.89%.

Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%.