Sub-pixel Classification Research Articles

Advanced remote sensing technologies for tracking landscape changes and environmental conditions

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.

Application of Remote Sensing in Desertification Monitoring

Desertification has become one of the greatest international concerns in recent decades. This study mainly focuses on the significance and necessity of applying remote sensing to improve efficiency in desertification monitoring. Results show that applications of remote sensing in desertification monitoring have shown advantages and promises of development in the future. Satellite images and unmanned aerial vehicle technology are two major sources of remote sensing data. This study also contains specific applications of remote sensing techniques, including normalized difference vegetation index (NDVI), land cover classification, and spectral mixture analysis (SMA). NDVI measures the vegetation cover to reflect potential desertification but fails to offer a comprehensive picture of the desertification status of an area. While land cover classification identifies different types of land use to monitor desertification, track changes over time, and find potential environmental stressors, the technique requires huge investments in technology and labor. SMA, the sub-pixel classification technique, provides a detailed analysis while consuming significant computing resources and time. This study also provides prospects for future developments of data acquisition and analysis approaches in desertification monitoring using remote sensing. Making suggestions on future research topics and possible methodologies, this study hopes to inspire further research in this area to better deal with the global issue of desertification.

Modeling and Unsupervised Unmixing Based on Spectral Variability for Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in the sense that it requires a prior estimation of various parameters of the mixing model, which is constraining. We here proceed much further, by first analytically showing that the above model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF unsupervised (i.e., blind) unmixing method that we proposed in previous works to handle spectral variability. Such variability especially occurs when the sea depth significantly varies over the considered scene. We show that IP-NMF then yields significantly better pure spectra estimates than a classical method from the literature that was not designed to handle such variability. We present test results obtained with realistic synthetic data. These tests address several reference water depths, up to 7.5 m, and clear or standard water. For instance, they show that when the reference depth is set to 7.5 m and the water is clear, the proposed approach is able to distinguish various classes of pure materials when the water depth varies up to ±0.2 m around this reference depth, over all pixels of the analyzed scene or over a “subscene”: the overall scene may first be segmented, to obtain smaller depths variations over each subscene. The proposed approach is therefore effective and can be used as a building block in performing the subpixel classification of the sea bottom for shallow water.

Spectral pathways for effective delineation of mafic-ultramafic rocks by using PRISMA hyperspectral data

The Spectral Pathway Analysis (SPA) by using hyperspectral remote sensing data has a significant role in the demarcation of litho boundaries. The SPA is a 5 step continuous process 1) The reflectance spectral characterization through a controlled environment to the direct field sample along with satellite data acquisition 2) The generation of ICA and MNF 3) The endmember spectral extraction through the sub-pixel-based classification 4) The extraction of mineral characters from spectral absorption behavior 5) Mapping of litho units based on the integrative approaches by the data from the field, geology, and geochemical characters. In the present study, mafic and ultramafic complexes of the Bhavani shear zone which have a greater significance in the economically viable mineral aspects are taken into the consideration of lithological delineation by using PRISMA hyperspectral data. As part of SPA, ICA and MNF were generated using the PRISMA data and validated with geology, which shows a tremendous result in demarcating the lithological boundaries. Subpixel classification (PPI, SAM, LSU, MF, MTMF, TCIMF, and MITTCIMF) was performed and evaluated using ancillary data from field observation, XRF, and laboratory-based ASD spectra.

Sub-Pixel Mapping of Spectral Imagery Based on Deviation Information Measurement

Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in optimal abundance images. Due to simple structure and good robustness, most SPM approaches are according to the spatial dependence assumption; namely, the closer the spatial distance is, the more likely the subpixels belong to the same class. However, the existing SPM methods based on spatial dependence assumption cannot accurately measure the DI, which affects the accuracy of the final mapping result. To address this problem, the SPM based on DI measurement (DIM) is proposed in this work. The DIM uses a dual-scale spatial attraction model (DSAM) to process the coarse abundance images to obtain predicted abundance images. The fine prior images captured at different times from the same field of view are used to measure the deviation abundance images with the DI using a geographically weighted regression (GWR) model. The predicted abundance images and deviation abundance images are fused to derive optimal abundance images. Based on the proportion information on sub-pixels being classes in the optimal abundance images, the class labels are assigned to sub-pixels by label allocation method, yielding the final mapping result. The proposed DIM method is tested on National Land-cover Dataset, Rome Dataset, and Bastrop Fires dataset. The experimental results verify that the proposed DIM achieves the best performance with the overall accuracy of 97.26%, 88.15%, and 99.80% in the three experimental results.

Individual and combined impacts of urbanization and climate change on catchment runoff in Southeast Queensland, Australia

Assessing the impacts of climate change and land-use change is of critical importance, particularly for urbanized catchments. In this study, a novel framework was used to examine and quantify these impacts on the runoff in six catchments in Southeast Queensland, Australia. For each catchment, temporal variations in impervious areas were derived from six satellite images using a sub-pixel classification technique and incorporated into the SIMHYD hydrological model. This model was satisfactorily calibrated and validated with daily runoff observations (0.63 ≤ Nash–Sutcliffe efficiency coefficient ≤ 0.94, percent bias ≤ ±18 %) and was used to produce baseline runoff for 1986–2005 in these six catchments. The projected population increase was used to predict future imperviousness based on the linear relationship between the two. The projected rainfall and evapotranspiration were derived from the ensemble means of the eight general circulation models. Catchment runoff was projected under two climate change scenarios (RCP4.5 and 8.5), three urbanization scenarios (low, medium, and high), and six combined scenarios for two future periods (2026–2045 and 2046–2065). Comparing with the baseline, it was found that (1) climate change alone would lead to a −3.8 % to −17.6 % reduction in runoff among the six catchments, for all scenarios and both future periods; (2) a 11.8 % to 78 % increase in runoff was projected under the three urbanization scenarios, and (3) a decrease in runoff due to climate change would moderate the increase in runoff caused by urbanization. For example, the combined effect would be a 54 % increase in runoff, with a −17.2 % decrease due to climate change and 78 % increase due to urbanization. Overall, runoff in the six catchments may be significantly affected by urban expansion. From this study, decision makers could gain a better understanding of the relative importance of the effects of climate and land-use change, which can be applied when developing future long-term water management plans at the catchment scale.

A Comparative Analysis of Methods of Endmember Selection for Use in Subpixel Classification: A Convex Hull Approach.

Mixed pixels in aerial and satellite images are common, especially near the boundaries of two or more discrete classes; that is, they tend to occur at the transitional region between two classes. Ideally, to decipher the mixed pixel, a soft classification is performed compared to a hard- or a per-pixel classification. Soft or subpixel classification is carried out where the fractional cover of the LULC contained within a pixel is derived. Endmembers are extracted for three VNIR bands of ASTER data for two image datasets using three approaches, namely, principal component analysis (PCA), pixel purity index (PPI), and convex hull-Graham scan (CHGS). On comparing the DN values of the identified endmembers, it is observed that the CHGS method provides the most appropriate end members than the PCA-derived and PPI-derived end members. This is based on deriving the endmembers from two different image conditions. Convex hull implemented using the Graham scan algorithm delineates the pure pixel and pinpoints the exact number of endmembers. These accurate end members would result in accurate proportions of the land cover for better modeling of the terrain.

Satellite-derived spatiotemporal data on imperviousness for improved hydrological modelling of urbanised catchments

• Performance of both lumped and distributed conceptual rainfall-runoff models were improved dramatically when temporal variations in impervious surface fraction (ISF) were used as input to these models. • Both SIMHYD and AWBM could be used for assessing urbanisation effect on streamflow of urbanized catchments. • Both lumped and distributed conceptual rainfall-runoff models showed similar performance in simulation of daily streamflows for the small urban catchment tested. • Given the observed rainfall for the study catchment, one percent increase in impervious surface fraction would lead to 6.9–7.3 mm (2.9–3%) increase in the annual streamflow during 1991–2018. Conceptual rainfall-runoff models have been used extensively to simulate streamflow in urban catchments. However, spatiotemporal changes in the impervious surface area, as an important land use indicator for urban environment, has not been used explicitly as input into conceptual rainfall-runoff models. In this study, spatiotemporal impervious surface fraction (ISF) data were incorporated into two lumped and distributed (1 km) models, namely SIMHYD and AWBM in an urbanized catchment in southeast Queensland, Australia. A sub-pixel classification technique was used to derive ISF from 6 LandSat images between 1988 and 2015. Daily rainfall-runoff models were calibrated with ISF as an input variable. To evaluate model performance for different simulation periods, four model runs including M1 (lumped models with constant ISF), M2 (lumped models with temporally varying ISF), M3 (distributed models with fixed spatial ISF) and M4 (distributed models with spatiotemporal ISF and climate data). Results showed that (1) the sub-pixel classification algorithm can be used to accurately derive ISF data with a mean absolute error (MAE) ranging from 0.07 to 0.12 for 6 images, (2) ISF in the catchment has increased gradually from 12.2% to 34.6% from 1988 to 2015, (3) the performance of SIMHYD and AWBM were not improved noticeably when distributed climate and ISF data were used, (4) simulation capability of lumped and distributed SIMHYD and AWBM were improved (e.g., increase in NSE from 0.68 to 0.73, and decrease in PBIAS from −30.2% to 1.2% for lumped SIMHYD) when temporal (M2) and/or spatiotemporal (M4) ISF data (5-year) were used, (5) difference in model performance is small and insignificantly whether annual or 5-yearly ISF data were used and (6) annual streamflow would increase by 6.9–7.3 mm (equal to 2.9–3%) in response to every 1% increase in ISF for the catchment tested. The methodology developed in this study can be applied for assessment of the effect of urbanisation on regional water balance.

Detecting the distribution of grass aboveground biomass on a rangeland using Sentinel-2 MSI vegetation indices

Quantifying the grass biomass content of rangelands is potentially useful to rangeland managers. This study assessed the potential of image-derived vegetation indices to assess in-situ spatial variations in grass aboveground biomass (AGB). Sampling was conducted at the peak growth stage, at widely distributed sampling sites whose grass cover homogeneity was wider than 20 m. Up to three AGB samples per site were collected, in 1 m quadrats. Dry biomass weights were determined and averaged per site. Rainy season Sentinel-2 MSI images of the rangeland that corresponded with the fieldwork dates were obtained. Atmospheric correction of the images was performed using the Sen2Cor algorithm, which yielded 20 m resolution visible (vis), red edge (RE), near infrared (NIR), and short wave infrared (SWIR) bands. Seven biomass-sensitive vegetation indices that utilised Sentinel-2 MSI vis-RE-NIR-SWIR spectral ranges were then tested using the images, to determine which correlated best with the field-derived AGB. They were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge index (NDRE), Red Edge Inflection Point (REIP), Aerosol Free Vegetation Index (AFRI 2.1μm ), and Normalized Difference Water Index (NDWI). In decreasing order of magnitude, the EVI, SAVI, NDVI, AFRI 2.1μm , and NDWI had statistically significant correlations with AGB ( p ≤ 0.05). The discontinuous canopies but high biomass of tall-grass species weakened the correlations. The EVI model of AGB was then used for depicting rangeland-scale, location-context spatial variations in AGB. Pixels with woody vegetation cover ≥ 50% were excluded using a binary mask that was developed through sub-pixel classification of a grass-senescence period Sentinel-2 MSI image. A statistically significant linear relationship ( F = 21.192, p = 0.000) between EVI-predicted and actual AGB was established ( r = 0.765, p < 0.001). The results from the studied savannah rangeland suggest higher vegetation index prediction accuracy at short-grass than tall-grass sites.

Image Pan-Sharpening and Sub-pixel Classification Enabled Building Detection in Strategically Challenged Forest Neighborhood Environment

Image Pan-Sharpening and Sub-pixel Classification Enabled Building Detection in Strategically Challenged Forest Neighborhood Environment

Projecting the urbanization effect on soil organic carbon stocks in polar and steppe areas of European Russia by remote sensing

This paper describes the effect of urbanization on soil organic carbon (SOC) stocks by projecting the main urban land cover classes over the initial pre-urban soil maps. Two cities different in climate and soil conditions as well as in availability of SOC data were chosen as the case studies. Rostov-on-Don is the center of Russian South, where croplands and natural steppes have been conventionally thoroughly studied by soil scientists. In contrast, soils of Murmansk located in Russian Arctics have always been overlooked due to low suitability for agriculture. Global, national and regional soil maps and databases were used to estimate pre-urban SOC stocks in the areas. The outcomes based on Harmonized World Soil Database were highly uncertain, underestimating 0-100 cm SOC stocks in the polar region and overestimating them in the steppe region, whereas the results based on Digital Soil Map of Russia and regional maps were comparable. Land cover structures of Rostov-on-Don and Murmansk were mapped based on the stepwise per-pixel and sub-pixel classification algorithms applied to Sentinel-2 and included the following classes: sealed soils, green lawns, trees and shrubs, bare soils and water. Murmansk was dominated by trees and shrubs (58.1%) with the proportion of area 17.5% covered by sealed soils. In Rostov-on-Don, less than 30% of the total area was covered by trees and shrubs which is also comparable with bare soils (19.6%)_and lawns (23.4%), whereas almost one third of the territory was sealed (27.6%). These land cover structures had a different impact on the topsoil SOC stocks: a 30-50% increase in Murmansk compared to the 18% decrease in Rostov-on-Don. An increase of the 0-100 cm SOC stocks was shown for both regions, however in the polar conditions it was two times higher compared to the steppe. In polar conditions, conversion of natural soils into urban non-sealed soils increased SOC stocks from 30% to more than 4 times in 0-10 cm layer and from 47% to almost 3 times in the 0-100 cm layer. The highest increase was reported for the lawns, whereas SOC under trees and shrublands were considerably lower. In Rostov-on-Don , sealed and bare soils stored less SOC compared to the initial natural soils. The conversion of natural areas into urban green infrastructure increased SOC up to 50-70%. Although the absolute SOC values based on the global and national legacy data are highly uncertain, especially for the polar areas, the research outcomes clearly reveal possible patterns in SOC changes induced by different urbanization pathways in contrast climatic conditions and highlight the complexity of the urbanization effect on soils

Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images.

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops.

Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of 'mixed pixels', which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.

Subpixel Analysis of MODIS Imagery Time Series using Transfer Learning and Relative Calibration

Transfer learning reuses a pre-trained model on a new related problem, which can be useful for monitoring large areas such as the Amazon biome. A given object must have similar spectral characteristics in the data used for this type of analysis, which can be achieved using relative calibration techniques. In this article, we present a relative calibration process in multitemporal images and evaluate its impacts on a subpixel classification process. MODIS images from the Amazon region, collected between 2013 and 2017, were relatively calibrated using a 2012 image as reference and classified by transfer learning. Classifications of calibrated and uncalibrated images were compared with data from the PRODES project, focusing on forest areas. A great variation was observed in the spectral responses of the forest class, even in images of proximate dates and from the same sensor. These variations significantly impacted the land cover classifications in the subpixel, with cases of agreement between the uncalibrated data maps and PRODES of 0%. For calibrated data, the agreement values ​​were greater than 70%. The results indicate that the method used, although quite simple, is adequate and necessary for the subpixel classification of MODIS images by transfer learning.

Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data

ABSTRACT Spaceborne data with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short Wave Infrared (SWIR) bands have been used worldwide in discriminating different clay minerals. However, due to wide bandwidth of ASTER sensors, it is difficult to distinguish between pure and impure variety of talc mineral. The present study highlights the efficacy of airborne hyperspectral data Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) for spectral analysis in talc mineral in the Jahazpur Belt of Bhilwara district in Rajasthan, India. Due to its better spectral and spatial resolution, AVIRIS-NG is found to be extremely useful in identifying talc mineral in SWIR region. Talc is characterized by doublet shaped spectral pattern in AVIRIS-NG image with strong absorption dips at 2289.75 nm and 2314.80 nm which could not be picked up in ASTER image. Spectral matching of talc pixels is verified with the field spectra of samples and available spectral library of United States Geological Survey, Jet Propulsion Laboratory, and International Geological Correlation Programme. Variation in reflectance percentage of different talc pixels is attributed to variation in chemical composition of talc. However, a large number of mixed pixels with spectral pattern of both talc and kaolinite minerals have been identified in AVIRIS-NG image which are found to be an impure variety of talc. Whole-pixel to sub-pixel classification techniques with Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, and Linear Spectral Unmixing (LSU) techniques have been applied for talc abundance mapping in Jahazpur area. LSU is found to be the most useful and accurate technique in classifying spectrally pure to impure variety of talc.

General solution to reduce the point spread function effect in subpixel mapping

The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing as pre-processing. To demonstrate the advantages of the new technique it was necessary to develop a new approach for accuracy assessment of SPM. To-date, accuracy assessment for SPM has been limited to subpixel classification accuracy, ignoring the performance of reproducing spatial structure in downscaling. In this paper, a new accuracy index is proposed which considers SPM performances in classification and restoration of spatial structure simultaneously. Experimental results show that by considering the PSF effect, more accurate SPM results were produced and small-sized patches and elongated features were restored more satisfactorily. Moreover, using the novel accuracy index, the quantitative evaluation was found to be more consistent with visual evaluation. This paper, thus, addresses directly two of the longest standing challenges in SPM (i.e., the limitations of the PSF effect and accuracy assessment undertaken only on a subpixel-by-subpixel basis).

Effective Morphological Transformation and Sub-pixel Classification of Clustered Images

The main aim of this research work is to perform the morphological operations with reduced time complexity and area complexity. Morphological operation is the key element in any image processing. Finding the maximum and minimum using a window of defined size will imply to the morphological dilation and erosion respectively. So the proposed algorithm should be fast in the comparison and sorting, this way the time complexity could be reduced. It’s believed that the anchor concept will fetch this cause. The idea behind this is it fixes a pixel and setting it as the center pixel all the surrounding pixels will be processed. Moreover this is now been implemented for rectangular structuring element. This paper attempts the same for flat and 3D structuring elements. Hyper-spectral Imaging is a developing zone of remote detecting applications. Hyper-spectral pictures incorporate more extravagant and better otherworldly data than the multi-spectral pictures got previously. Hyper-otherworldly pictures are described by an exchange off between the unearthly and spatial resolution. The principle issue of the hyper-ghostly information is the generally low spatial goal. For arrangement, the serious issue brought about by low spatial goal is the blended pixels. Blended pixels alluded to the pixels which are involved by more than one land spread class. In the proposed procedure another strategy is utilized to address the issue of blended pixels and to get a better spatial goal of the land spread characterization maps. The strategy misuses the upsides of both picture bunching methods and phantom dimming calculations, so as to decide the fragmentary plenitudes of the classes at a sub-pixel scale. Spatial regularization by Flank planning method is at last performed to spatially find the got classes at sub-pixel level.

Detection of snow/ice cover changes using subpixel-based change detection approach over Chhota-Shigri glacier, Western Himalaya, India

Mapping and monitoring of the glacier changes over different regions of Earth surface is a challenging task due to regional rugged topography and climate conditions. This study focused on the monitoring of snow or ice cover changes over Chhota-Shigri glacier, Western Himalaya, India. A subpixel-based change detection (SCD) approach is proposed, aiming to identify the transition zones (mixed pixels) between the two class categories. The SCD approach involves the integration of subpixel classification and change vector analysis (CVA) to define the changes in the form of magnitude and direction between two multitemporal dates at the subpixel level. To check the efficacy of proposed SCD, experimental outcomes have also been compared with existing neural-network (NN) based SCD (NN‒SCD). The result analysis has shown that proposed SCD achieved better accuracy (84.80%) as compared to NN‒SCD (78.80%). In addition, a time series data was acquired using the Landsat series (Landsat 5, 7 and 8 as per availability) to perform the trend analysis over Chhota-Shigri glacier, during the period 2001–2019. This study offers the effective way of estimating the bi-temporal snow/ice changes especially over rugged terrains around the globe.

On Drivers of Subpixel Classification Accuracy—An Example From Glacier Facies

Subpixel classification (SPC) extracts meaningful information on land-cover classes from the mixed pixels. However, the major challenges for SPC are to obtain reliable soft reference data (RD), use apt input data, and achieve maximum accuracy. This article addresses these issues and applies the support vector machine (SVM) to retrieve the subpixel estimates of glacier facies (GF) using high radiometric-resolution Advanced Wide Field Sensor (AWiFS) data. Precise quantification of GF has fundamental importance in the glaciological research. Efficacy of the approach was first tested on the synthetic data followed by the input AWiFS and reference MultiSpectral Instrument data, including ancillary data. SPC of synthetic data resulted in overall accuracy (OA) of 95%, proving the merit of SVM. Classification accuracy is inversely related to the glacier's surface heterogeneity. Reducing the number of classes enhanced the OA by ∼18%. Source and timing of RD invariably controls the SPC accuracy. OA improved by ∼5% on addressing the issue of temporal gap between input and RD. ∼11% increase in OA with the inclusion of ancillary data confirmed their positive effect on the accuracy. Input and reference fractional area of GF were strongly correlated ( r > 0.9) with each other substantiating the results.

LINEAR SPECTRAL UNMIXING OF SENTINEL-3 IMAGERY FOR URBAN LAND COVER - LAND SURFACE TEMPERATURE (LST) ANALYSIS: A CASE STUDY OF METRO MANILA, PHILIPPINES

Abstract. The advancement of remote sensing technologies is a huge advantage in various environmental applications including the monitoring of the rapid development in an urban area. This study aims to estimate the composition of the different classes (vegetation, impervious surfaces, soil) in Metro Manila, Philippines using a 300-meter spatial resolution Sentinel-3 Ocean and Land Colour Instrument image. The relationship between these land cover fractions with the spatial distribution of land surface temperature at this scale is evaluated. Sentinel-3 image has a higher spectral resolution (i.e. 21 bands), as compared with other Landsat and Sentinel missions, which is a requirement for an accurate cover mapping. Linear Spectral Unmixing (LSU), a sub-pixel classification method, was employed in identifying the fractional components in the image based on their spectral characteristics. Field survey using spectroradiometer was conducted to acquire spectral signatures of an impervious surface, vegetation, and soil which were used as the endmembers in the unmixing process. To assess the accuracy of the resulting vegetation fractional image, this was compared with a separate land cover pixel-based classification result using a 3-meter high spatial resolution PlanetScope image and with another vegetation index product of Sentinel-3. The results indicate that the recently available Sentinel-3 image can accurately estimate vegetation fraction with R2 = 0.84 and 0.99, respectively. In addition, the land surface temperature (LST) retrieved from Climate Engine is negatively correlated with the vegetation fraction cover (R2 = 0.81) and positively correlated with the impervious surface fraction cover (R2 = 0.66). Soil, on the other hand, has no correlation with the LST.