ArcIndices: a toolbox for computing spectral indices from multiband satellite imagery in ArcGIS environment

In post-flood disaster analysis, accurate flood mapping in complex riverine urban areas is critical for effective flood risk management. Recent studies have explored the use of water-related spectral indices derived from satellite imagery combined with machine learning (ML) models to achieve this purpose. However, relying solely on spectral indices can lead these models to overlook crucial urban contextual features, making it difficult to distinguish inundated areas from other similar features like shadows or wet roads. To address this, our research explores a novel approach to improve flood segmentation by integrating a row-wise cross attention (CA) module with ML ensemble learning. We apply this method to the analysis of the Brisbane Floods of 2022, utilizing 4-band satellite imagery from PlanetScope and derived spectral indices. Applied as a pre-processing step, the CA module fuses a spectral band index into each band of a peak-flood satellite image using a row-wise operation. This process amplifies subtle differences between floodwater and other urban characteristics while preserving complete landscape information. The CA-fused datasets are then fed into our proposed ensemble model, which is constructed using four classic ML models. A soft voting strategy averages their binary predictions to determine the final classification for each pixel. Our research demonstrates that CA datasets can enhance the sensitivity of individual ML models to floodwater in complex riverine urban areas, generally improving flood mapping accuracy. The experimental results reveal that the ensemble model achieves high accuracy (approaching 100%) on each CA dataset. However, this may be affected by overfitting, which indicates that evaluating the model on additional datasets may lead to reduced accuracy. This study encourages further research to optimize the model and validate its generalizability in various urban contexts.

Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery

Recent innovations in satellite imaging have significantly improved the effectiveness of Earth observation through high-resolution imagery used in environmental analysis, urban ecosystem, city planner mapping, and disaster management. Nevertheless, with high-dimensional Landuse Landcover multispectral data available, the classification of satellite images remains a challenging task due to the variability of the data. The conventional approaches to machine learning do well but fail at managing the intricacies of multispectral data without shredding and feature extraction. This research presents a novel technique by applying the VGG-16 structure, a deep convolutional neural network for image recognition, to classify the EuroSAT multispectral satellite image dataset, consisting of various European terrains captured across multispectral bands, including visible and near-infrared, which are essential for environmental analysis. The proposed technique presents a VGG-16 model enhanced with new convolutional blocks and dropout layers, specifically designed to classify multiband satellite imagery data. The first phase of convolutions has been made to work with four spectral bands, more specifically, RGB and near-infrared, to increase the capacity of distinguishing the type of ground cover. In addition, the output layer has improved to provide a ten-class scene classification (forest, residential, industrial, highway, pasture, river, sea lake, herbaceous vegetation, annual crop, permanent crop) for various landcover types, enhancing the model's applicability. Augmentation techniques, including rotation, flipping, and shifting, have been used to increase the diversity of the training dataset; additionally, transfer learning leveraged the resultant augmented datasets. This adaptation not only enhances the mechanism for classifying the satellite images but also decreases the time and computation resources needed, thereby making it applicable to big data. The proposed modifications to the VGG-16 allow obtaining a higher classification accuracy rate of ±96.7%, which ensures the effectiveness of an automated system for analyzing satellite imagery. Received: 15 November 2024 | Revised: 10 March 2025 | Accepted: 14 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The EuroSat data that support the findings of this study are openly available in GitHub at https://github.com/phelber/eurosat. Author Contribution Statement Ranu Sewada: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization. Hemlata Goyal: Conceptualization, Formal analysis, Writing – review & editing, Supervision, Project administration.

This study presents an integrated remote sensing approach for assessing the ecological consequences of the destruction of the Kakhovka Reservoir in Southern Ukraine. The methodology combines spectral vegetation indices, principal component analysis, and Procrustean analysis to evaluate spatial and functional transformations in vegetation cover following a large-scale anthropo genic disaster. The approach was applied to floodplain ecosystems on Khortytsia Island and adjacent areas using satellite imagery from the Sentinel-2 mission for the years 2022 and 2024. A set of twenty-nine spectral indices, sensitive to vegetation density, pigment composition, water conditions, and soil properties, was employed to identify patterns in plant community dynamics and environmental change. Principal component analysis was utilized to identify the dominant axes of spectral variability, while Procrustean rotations facilitated the detection of significant spatial shifts over time. The results demonstrated strong correlations between changes in vegetation patterns and key ecological indicators, including hemeroby, naturalness, species richness, and functional diversity. Two primary ecological trends were identified. The first trend is associated with ecosystem degradation due to anthropogenic pressure, characterized by increasing hemeroby, a decline in naturalness, and reductions in both functional evenness and functional divergence. The second trend reflects the internal reorganization of plant communities under near-natural conditions, where increases in projective cover and species richness occur alongside a decrease in functional richness. Spectral ind ices, such as the normalized difference vegetation index, the normalized difference chlorophyll index, the red-edge vegetation index, the normalized difference tillage index, and the normalized difference water index, have proven particu larly effective in detecting both degradation and successional processes. This study demonstrates that satellite-based spectral indices can serve as reliable proxies for assessing the functional structure and ecological condition of vegetation. The proposed methodology provides an effective tool for spatially explicit and timely environmental monitoring, thereby supporting evidence-based decision-making in post-disaster landscape management, including the question of restoring water bodies or conserving newly formed floodplain ecosystems. This approach has broad applicability for long-term ecological monitoring, restoration planning, and adaptive ma n agement in regions impacted by significant anthropogenic transformations.

A study was carried out to assess the potential use of satellite thematic mapper (TM) images to produce maps of vegetation‐related variables for erosion modelling. In a Mediterranean study area in southern France the (semi‐)natural vegetation was described at 33 field plots using four quantitative methods: the Fosberg structural classification system, the cover and management factor of the Universal Soil Loss Equation, the leaf area index and the total percentage cover. After radiometric correction of the image, the spectral TM bands were processed following three different methods. Each method aimed at combining the data of the six spectral TM bands into a single band in such a way that the resulting image displayed optimal information on green vegetation cover. The algorithms used comprise the normalized difference vegetation index, the conventional ‘tasselled cap’ transformation and a locally tuned tasselled cap transformation. Only slight differences were found between the different methods to calculate spectral vegetation indices for this particular case. Furthermore, the correlations between the field variables and image‐derived spectral indices are generally small. The largest correlations were found for the normalized vegetation index and the leaf area index (r + 0·71) and for the normalized vegetation index and Fosberg's structural vegetation classes (r + 0·76). However, Fosberg's method results in very general classes, which are of little use for soil erosion models. Furthermore, the spectral indices appeared to be sensitive for the vitality of the vegetation. Consequently, an area covered by a sensed, senescent vegetation will not yield a large value for the spectral index, but its soil is protected against splash erosion. This might lead to a misinterpretation of the soil protective cover when satellite images are used. A final conclusion is that a balance has to be found between the more accurate, but time‐consuming field surveys to gather information on erosion‐controlling factors and a certain loss of accuracy associated with the use of quick and easy remote sensing methods.

Durante la última década, ha habido un número creciente de trabajos publicados sobre la gravedad de los incendios forestales utilizando datos de teledetección para fines de gestión de recursos naturales y de investigación. Muchos de estos estudios cuantifican los cambios entre las condiciones de vegetación antes y después del incendio a partir de imágenes satelitales utilizando índices espectrales; sin embargo, hay una discusión activa sobre cuál de los índices más comúnmente usados es más adecuado para estimar la severidad de la quemadura, y qué metodología es la mejor para la estimación de los niveles de severidad. Este estudio propone y evalúa un algoritmo de aprendizaje automático de Estimación de Máxima Verosimilitud (EMV) para mapear la severidad de las quemaduras como una alternativa a los modelos de regresión.Desarrollamos ambos métodos usando datos de campo de GeoCBI (Índice Compuesto de Quema Geométricamente Estructurado, siglas en inglés) y seis índices espectrales diferentes (derivados de imágenes Landsat TM y ETM+) para dos incendios forestales en el centro de España. Comparamos la capacidad para discriminar la severidad de la quemadura de estos índices a través de un índice de separabilidad espectral (M), y evaluamos su concordancia con datos de campo basados en GeoCBI usando el coeficiente de determinación (R2). Posteriormente, el índice seleccionado se utilizó para los modelos de regresión y la EMV para estimar los niveles de severidad de quema (sin quemar, bajo, moderado y alto), y se validó con datos de campo. El índice RBR mostró una mejor separabilidad espectral (promedio entre dos fuegos M= 2.00) que el dNBR (M= 1.82) y RdNBR (M= 1.80). Además, GeoCBI tuvo un mayor ajuste con RBR (R2= 0.73) que con RdNBR (R2= 0.72) y dNBR (R2= 0.71).Finalmente, la EMV mostró la mayor precisión de clasificación general (Kappa=0,65) y la mejor precisión para cada clase individual.

The accurate and rapid inversion of soil salinity in regions based on the fusion of multisource remote sensing is not only practical for the treatment and utilization of saline soil but also the main trend in the development of quantitative soil salinization remote sensing. In this paper, the use of a numerical regression method to fuse spectral indexes based on high-spatial-resolution unmanned aerial vehicle (UAV) images and low-spatial-resolution satellite images was proposed to deeply assess the internal relationships between different types of remote sensing data. An inversion model of soil salt content (SSC) was constructed based on high-spatial-resolution UAV images, and the spectral indexes involved in the fusion were selected from the model. Then, a quadratic polynomial fusion function describing the relationship between the spectral indexes based on the two images was established to correct the spectral indexes based on the low-spatial-resolution satellite image (from Sentinel-2A). Then, scenario 1 (the best model based on Sentinel-2A used for the unfused Sentinel-2A spectral index), scenario 2 (the best inversion model based on UAV used for the unfused Sentinel-2A-based spectral index), and scenario 3 (the best inversion model based on UAV used for the fused Sentinel-2A-based spectral index) were compared and analyzed, and the SSC distribution map was obtained through scenario 3. The results indicate that the scenario 3 had highest accuracy, with the calibration R 2 improving by 0.078-0.111, the root mean square error (RMSE) decreasing by 0.338-1.048, the validation R 2 improving by 0.019-0.079, the RMSE decreasing by 0.517-1.030, and the ratio of performance to deviation (RPD) improving by 0.185-0.423. Therefore, this method can improve the accuracy of SSC remote sensing inversion, which is conducive to the accurate and rapid monitoring of SSC.

Logging is a major form of forest degradation in the tropical regions like Brazilian Amazon. It alters the tropical habitat environments and results in release of carbons as well. The traditional way of logging is through forest clearing, which converts forest to other land uses such as agriculture or rangeland. Recently a new form of forest degradation is selective logging, removing only those good quality tree species. This form of deforestation does not result in land use conversion, but degradation. Logging by means of clear-cutting can be easily detected and monitored from satellite images such as those from Landsat sensors. However, detection and monitoring selective logging is difficult with satellite images because the process only removes a small number of trees per area, resulting in subtle disturbances but substantial removal of biomass. Therefore, traditional classification technique is unable to detect and monitor this type of disturbances effectively. In order to detect selective logging, and to better understand carbon sequestrations, a continuous field ought to be used that can quantify the degree of disturbances due to selective logging, instead of using binary classification techniques. In this paper, we used signal-unmixing techniques in a spectral vegetation index domain as a continuous field measure of forest density, with which selective logging is mapped and quantified. The spectral index used is the MSAVI further modified to enhance its sensitivity to subtle forest degradations in the tropical environments in Brazilian Amazon as well as in Southeast Asia.

Advances in satellite image acquisition systems and data availability present exciting opportunities for solar irradiance analysis, especially regarding albedo irradiance which becomes a significant energy harvest contributor when considering bifacial PV modules. This paper demonstrates a methodology for wide area albedo irradiance assessment using multiband (spectrally resolved) satellite imagery. A sample 10-band multispectral image of a 0.75 km2 region with a pixel resolution of 0.25 m2 is used to demonstrate the localized, contextual nature of spectral reflections. Combining spectral reflection data with silicon spectral response curves shows variations in the sample region of 30% in reflected conversion efficiencies. Variations in average photon energy of approximately 0.05 eV are found across the sample region for spectra with 30% additional reflected contributions.

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices

Determining residue cover on agricultural land is an important task. Residue cover helps reduce soil erosion and helps sequester carbon. Many studies have used either spectral indices or classification techniques to map residue cover using satellite imagery. Unfortunately, most of these studies use only a few spectral indices or classification techniques and generally only study an area for a single year with a certain level of success. This manuscript presents an investigation of five spectral indices and six classification techniques over four years to determine if a single spectral index or classification technique performs consistently better than the others. A second objective is to determine whether using the coefficient of determination (R2) from the relationship between residue cover and a spectral index is a reasonable substitute for calculating accuracy. Field visits were conducted for each of the years studied and used to create the correlations with the spectral indices and as ground truth for the classification techniques. It was found that no spectral index/classification technique is consistently better than all the others. Classification techniques tended to be more accurate in 2011 and 2013, while spectral indices tended to be more accurate in 2015 and 2018. The combination of spectral indices/classification techniques outperformed the individual approach. For the second objective, it was found that R2 is not a great indicator of accuracy. Root mean square error (RMSE) is a better indicator of accuracy than R2. However, simply calculating the accuracy would be the best of all.

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

With the advent of high-resolution remote sensing images, automatic building extraction methods play a more important role in rapidly acquiring information about large-scale buildings. Although advanced building extraction methods have been introduced to improve building extraction results, these methods involve complex processing and high-computation times. We put forward an effective method to extract building information, based on a proposed spectral building index. The basic idea of the spectral building index is to generate an optimized index based on the computation and analysis of spectral bands, which are beneficial for image enhancement for buildings in images. Aiming at the band number of the multispectral satellite images in high-resolution remote sensing images, we propose two spectral indices for building extraction, including the normalized spectral building index (NSBI) and the difference spectral building index (DSBI). Considering the current spectral band number of high-resolution satellite images, NSBI is suited for satellite images with eight spectral bands, whereas DSBI is suited for satellite images with four spectral bands. The proposed method is validated on various high-resolution images including WorldView-2, GF-1, GF-2, and QuickBird images with 13 experiment datasets, as well as a detailed comparison to the state-of-the-art methods, such as the morphological building index, nonhomogeneous feature difference, and building condition index. The experimental results reveal that the proposed method can achieve promising results for different building conditions, such as regular and irregular building shapes and concrete and metal roofing materials. The average overall accuracy was over 85% with low-time consumption (<1 s).

The emerging threat for eco-sustainability has led to a breakthrough in satellite image analyses and such instantaneous monitoring of hazards could replenish the rejuvenation of natural ecosystem. Generally, the satellite images are huge dimensional data with numerous bands of specific details about the observed region. To apply immediate precautionary measures for environmental hazards and natural devastations, deploying a cloud-based intelligent web service for handling real time satellite image processing is inevitable. Therefore, the cloud implementation could afford integrated huge storage and parallel data processing tasks, the outcome of instantaneous satellite image processing relies with the effective data processing methods of less complexity. In this regard to address a major hazard of today which is drought monitoring, this paper focuses on developing an effective water segmentation method for such geospatial cloud web services. The Landsat 8 images of Sambhar lake region has been chosen for exploiting the water segmentation results. Most prevalent approaches from Spectral indices and unsupervised clustering such as normalized difference water index (NDWI), modified normalized difference water index, fuzzy C means, K-Means, Adaptive Regularized kernel fuzzy C means (ARKFCM) and simple linear iterative clustering-based superpixel segmentation (SLIC-SUPER) are compared, respectively. On comparative assessment using standard image quality assessment metrics, NDWI and ARKFCM outstands the rest with more accurate water body delineation. However, based on reduced computational complexity and instant localization, NDWI of spectral indexing approach clearly portray the significance of spectral influence in water body segmentation from satellite images. And it can be adapted as a persistent choice for instantaneous water body segmentation in a cloud-centered geospatial module.