Spectral Index Research Articles

Annual improved maps to understand the complete evolution of 9 thousand lakes on the Tibetan plateau in 1991–2023

Rapid changes in the densely distributed lakes on the Tibetan Plateau (TP) reflect the responses of terrestrial water resources to climate change. Timely and accurate monitoring of lake dynamics is essential for formulating adaptation strategies to manage water and protect public facility safety sustainably. Interfered by the numerous glaciers and snow mountains and limited by the acquisition and computing capacities of massive satellite data, annual inventories of all the lakes ranging from mini to large on the TP are still lacking. Here, we annually mapped these lake areas using all the Landsat imagery, a robust algorithm for detecting surface water according to multiple spectral indices, and Google Earth Engine. We further proposed an effective approach for accurately identifying the glaciers, snow, and mountain shadows in satellite imagery by introducing the characteristics of image luminosity and terrain slope, and removing their data noise remained in the lake maps to generate an annual precise dataset (Lake_TP) of the approximately 9,000 lakes over 0.1 km2 on the TP during 1991–2023. We revealed a rapid expansion of lakes with significant spatial heterogeneity, with 6,590 newly increased and 2,851 disappeared lakes found. The total lake areas (554.1 km2/yr) and numbers (77.9/yr) continuously and significantly increased in the period. The growth in lake numbers dominated by small lakes mainly happened before 2005, while the increases in lake areas dominated by large lakes lasted the whole period after 1995. The most significant increases in lake areas and numbers happened in the north of the Inner Basin and Yangtze, the hotspot of lake changes identified in the study. The dataset is expected to promote our understanding of the complete lake evolution process and the dynamic response of the cryosphere to the changing climate. The method proposed is also applicable to continuously monitoring the dynamics of lakes with higher accuracies in other alpine regions around the world. The Lake_TP dataset is publicly available at https://doi.org/10.5281/zenodo.10686952 (Zhou et al. 2024).

Constraints on Primordial Magnetic Fields from High Redshift Stellar Mass Density

Primordial magnetic fields (PMFs) play a pivotal role in influencing small-scale fluctuations within the primordial density field, thereby enhancing the matter power spectrum within the context of the ΛCDM model at small scales. These amplified fluctuations accelerate the early formation of galactic halos and stars, which can be observed through advanced high-redshift observational techniques. Therefore, stellar mass density (SMD) observations, which provide significant opportunities for detailed studies of galaxies at small scales and high redshifts, offer a novel perspective on small-scale cosmic phenomena and constrain the characteristics of PMFs. In this study, we compile 14 SMD data points at redshifts z > 6 and derive stringent constraints on the parameters of PMFs, which include the amplitude of the magnetic field at a characteristic scale of λ = 1 Mpc, denoted as B 0, and the spectral index of the magnetic field power spectrum, n B . At 95% confidence level, we establish upper limits of B 0 < 4.44 nG and n B < −2.24, along with a star formation efficiency of approximately f*0∼0.1 . If we fix n B at specific values, such as −2.85, −2.9, and −2.95, the 95% upper limits for the amplitude of the magnetic field can be constrained to 1.33, 2.21, and 3.90 nG, respectively. Finally, we attempt to interpret recent early observations provided by the James Webb Space Telescope using the theory of PMFs and find that by selecting appropriate PMF parameters, it is possible to explain these results without significantly increasing the star formation efficiency.

Development of a new indicator for identifying vegetation destruction events using remote sensing data

Frequent climate change and intense anthropogenic activity increase the risk of vegetation destruction. Remote sensing technology, known for its timely observations and wide coverage, is a crucial tool for monitoring vegetation growth. However, accurately detecting vegetation destruction events remains challenging due to their spectral diversity, particularly in complex environments. Existing spectral indices (VIs) have limitations in effectively capturing vegetation dynamics as they are only sensitive to specific physiological parameters of vegetation, such as foliage, canopy, or water content, and are prone to background interference. To address this issue, we proposed the Slope Vegetation Index (SVI) based on Sentinel-2 imagery and PROSAIL model simulation data. Five representative VIs were selected for comprehensive comparison. The results showed that, compared with other VIs, SVI had the highest sensitivity to vegetation physiological parameters, with a correlation coefficient (R2) greater than 0.98. SVI performed best across all vegetation change scenes, with producer accuracy (PA), user accuracy (UA), and F1 score all exceeding 0.90. SVI proved effective in detecting various vegetation destruction events, including logging, insect infestation, landslides, and wildfires. Moreover, SVI was suitable for Landsat-8/9 imagery, achieving an F1 score of over 0.89. Overall, SVI is an effective and robust vegetation monitoring index, offering valuable insights for vegetation resource management and post-disaster ecological restoration.

Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality

Forests are crucial in delivering ecosystem services that underpin human well-being and biodiversity conservation. However, these vital ecosystems are threatened by forest degradation and rapid urbanisation. This study addresses this challenge by proposing a comprehensive framework for mapping natural forests at the municipal scale. The framework integrates remote sensing techniques with machine learning algorithms to provide valuable insights into the extent of natural forests within the eThekwini Municipality. The study utilised Landsat 7, Landsat 8, and Landsat 9 satellite imagery to analyse and map the historical and current distribution of natural forests. Five spectral indices, namely, Normalized Differential Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Index Green (CIG), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI-2), which were calculated from Landsat bands, were employed in the analysis. Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Extreme Gradient Boosting (XGBoost) machine learning algorithms were used to model forest distribution. Accuracy was assessed through confusion matrices, Receiver Operating Characteristic (ROC) Curves, area under the ROC curve (AUC), and the F1 scores. LightGBM achieved the highest overall accuracy (90.76%), followed by CatBoost (89.56%) and XGBoost (84.34%). LightGBM also obtained the best F1 score (90.76%). These findings highlight LightGBM's effectiveness in classifying natural forests, making it the preferred model for mapping the historical extent of natural forests in the eThekwini Municipality. However, classifications based on Landsat 7 significantly underestimated the extent of natural forests within the study area, whereas Landsat 8 and Landsat 9 data revealed an increase in natural forests from 2015 to 2023. These findings will guide effective and targeted forest rehabilitation and restoration efforts, ensuring the preservation and enhancement of forest ecosystem services.

Strange and Odd Morphology Extragalactic Radio Sources (STROMERSs): A Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) Look at the Strange and Odd Radio Sources

We report the identification of an extremely rare and peculiar set of irregular radio sources, termed “STROMERSs” (STRange and Odd Morphology Extragalactic Radio Sources).ingThe irregular radio sources with very anomalous morphologies that make them exceptionally different from all the known classes and subclasses of irregular radio sources are detected as STROMERSs. A thorough search for this class of sources from the Very Large Array (VLA) Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) gave a total of nine such candidates. We checked the corresponding morphology of the identified sources in other frequency surveys. We found a detectable radio emission for all of the nine sources in the NRAO VLA Sky Survey (NVSS) at 1.4 GHz and in the TIFR GMRT Sky Survey (TGSS) at 150 MHz, while the same was found for only three sources in the Westerbork Northern Sky Survey (WENSS) at 625 MHz. However, the strange morphology was not found in all of those other survey images. We also characterized the sources with their corresponding physical parameters like optical counterpart, size, spectral index, and radio luminosity. ingThe estimated spectral values of the sources indicated that the STROMERSs were most likely radio galaxies. The presence of any nearby galaxy clusters for the STROMERSs was also checked.

Long-term assessment of fire-induced carbon loss in Southeast Atlantic Forest

Fires threaten tropical forests such as Atlantic Forest in Brazil, compromising the ecosystem service of carbon stock. However, there is a literature gap regarding these studies in these ecosystems. Therefore, we conducted this analysis in different land use and land cover (LULC) classes, considering seasonality and topographic, hydrological, anthropogenic and fire variables correlations, during 2000-2020. The InVEST Carbon model was used, applied to carbon biomass pre-fire and pos-fire, based on field work and linear regression, weighted by pre- and post-fire NBR spectral index. The results, in 21 years, revealed a total loss after fire of 55.7GgC (43%), and of these, 79% is in old-growth Ombrophilous dense. In general, fire negatively impacts the carbon stock of native forests by an average of 38% (ranging from 19.9% to 69.1%, depending on phytophysiognomy and seasonality), Eucalyptus plantations by 87.1%, high-altitude grasslands by 79.5% and pasture in 90.4%. Burn frequency and severity as well as distance from rivers and roads were significantly correlated with carbon loss. A small portion of this biome has shown a high potential for fire-induced carbon loss, indicating a danger for the whole Atlantic Forest conservation and to international agreements commitments.

Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals.Graphical

Particle acceleration, escape, and non-thermal emission from core-collapse supernovae inside non-identical wind-blown bubbles

Context. In the core-collapse scenario, supernova remnants (SNRs) evolve inside complex wind-blown bubbles structured by massive progenitors during their lifetime. Therefore, particle acceleration and the emissions from these SNRs can carry the fingerprints of the evolutionary sequences of the progenitor stars. Aims. We investigate the impact of the ambient environment of core-collapse SNRs on particle spectra and emissions for two progenitors with different evolutionary tracks while accounting for the spatial transport of cosmic rays (CRs) and the magnetic turbulence that scatters CRs. Methods. We used the RATPaC code to model the particle acceleration at the SNRs with progenitors having zero-age main sequence (ZAMS) masses of 20 M⊙ and 60 M⊙. We constructed the pre-supernova circumstellar medium (CSM) by solving the hydrodynamic equations for the lifetime of the progenitor stars. Then, the transport equation for cosmic rays, the magnetic turbulence in test-particle approximation, and the induction equation for the evolution of a large-scale magnetic field were solved simultaneously with the hydro-dynamic equations for the expansion of SNRs inside the pre-supernova CSM in 1-D spherical symmetry. Results. The profiles of gas density and temperature of the wind bubbles along with the magnetic field and the scattering turbulence regulate the spectra of accelerated particles for both of the SNRs. For the 60 M⊙ progenitor, the spectral index reaches 2.4, even below 10 GeV, during the propagation of the SNR shock inside the hot shocked wind. In contrast, we did not observe a persistent soft spectra at earlier evolutionary stages of the SNR with the 20 M⊙ progenitor, for which the spectral index becomes 2.2 only for a brief period during the interaction of SNR shock with the dense shell of red supergiant (RSG) wind material. At later stages of evolution, the spectra become soft above ~10 GeV for both SNRs, as weak driving of turbulence permits the escape of high-energy particles from the remnants. The emission morphology of the SNRs strongly depends on the type of progenitors. For instance, the radio morphology of the SNR with the 20 M⊙ progenitor is centre-filled at early stages, whereas that of the more massive progenitor is shell-like.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

Analysing slope dynamics of Kaleköy (Türkiye) dam reservoir with Sentinel-1 SAR time series and Sentinel-2 spectral indices

Analysing slope dynamics of Kaleköy (Türkiye) dam reservoir with Sentinel-1 SAR time series and Sentinel-2 spectral indices

Estimating and Analyzing The Stability of Brazilian Gnss Stations for Tectonic Studies

The detailed study of stresses and strains is fundamental for geophysical and geodetic studies, enabling the development of plate motion models, geodynamic processes and several proposals. GNSS positioning has been used to estimate these stresses and strains. However, for the accuracy required, it is necessary a rigorous assessment of the behavior of GNSS stations and their position time series. This paper aims to analyze the influence of the different types of geodetic monuments present in the Brazilian GNSS Continuous Monitoring Network by estimating and characterizing noise in the time series and investigating variables that can affect the stability of the stations to establish criteria for using this data in tectonic and geodynamic studies. The evaluation of 119 GNSS stations throughout Brazil indicates the significant presence of random walk noise in some of these stations, detected by analyzing the magnitude of the noise and the spectral index. It was found that the stations with unstable monuments have been observed for at least eight years and are mostly in coastal areas or under the influence of large watercourses.

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Improved method for cropland extraction of seasonal crops from multi-sensor satellite data

ABSTRACT Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology.

Source Count Distribution of Fermi LAT Gamma-Ray Blazars Using Novel Nonparametric Methods

We utilized a sample from the Fermi-LAT 14-year Source Catalog by adjusting the flux detection threshold, enabling us to derive the intrinsic source count distribution dN/dF25 of extragalactic blazars using nonparametric, unbinned methods developed by Efron and Petrosian and Lynden-Bell. Subsequently, we evaluated the contribution of blazars to the extragalactic gamma-ray background. Our findings are summarized as follows: (1) There is no significant correlation between flux and spectral index values among blazars and their subclasses FSRQs and BL Lacs. (2) The intrinsic differential distributions of flux values exhibit a broken-power-law form, with parameters that closely match previous findings. The intrinsic photon index distributions are well described by a Gaussian form for FSRQs and BL Lacs individually, while a dual-Gaussian model provides a more appropriate fit for blazars as a whole. (3) Blazars contribute 34.5% to the extragalactic gamma-ray background and 16.8% to the extragalactic diffuse gamma-ray background. When examined separately, FSRQs and BL Lacs contribute 19.6% and 13% to the extragalactic gamma-ray background, respectively.

Weighted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi-Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.

Experimental analysis on the optimal spectral index for the risk assessment of red tide occurrence

Experimental analysis on the optimal spectral index for the risk assessment of red tide occurrence

Assessing the Correlation Between Spectral Indices and Land Surface Heat Fluxes by Remote Sensing Technology: A Case Study in Thai Binh Province, Red River Delta, Vietnam

Assessing the Correlation Between Spectral Indices and Land Surface Heat Fluxes by Remote Sensing Technology: A Case Study in Thai Binh Province, Red River Delta, Vietnam

The NANOGrav 15 yr Data Set: Chromatic Gaussian Process Noise Models for Six Pulsars

Abstract Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio frequency. However, pulse arrival times are also affected by radio-frequency-dependent “chromatic” noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect the achromatic noise properties in each pulsar. The models we compare include existing DM models used by the North American Nanohertz Observatory for Gravitational waves (NANOGrav) and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar data set. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from log 10 A RN = − 14.1 − 0.1 + 0.1 to − 14.7 − 0.5 + 0.3 , and the spectral index broadens from γ RN = 2.6 − 0.4 + 0.5 to γ RN = 3.5 − 0.9 + 1.2 . We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Machine learning-based maize (Zea mays L.) extraction at parcel level using Sentinel 2A-derived spectral indices

Machine learning-based maize (Zea mays L.) extraction at parcel level using Sentinel 2A-derived spectral indices

Analysing the capacity of multispectral indices to map the spatial distribution of potential post-fire soil losses based on soil burn severity

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions.