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Journal articles on the topic 'Severity Index and Remote Sensing'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

S., K. Singh, Dutta Sujay, and Dharaiya Nishith. "MAPPING OF COTTON MEALYBUG (HEMIPTERA: PSEUDOCOCCIDAE) DAMAGE IN SIRSA DISTRICT, HARYANA USING GEOSPATIAL TECHNIQUE." International Journal of Engineering Sciences & Research Technology 5, no. 3 (2016): 138–46. https://doi.org/10.5281/zenodo.47007.

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Detection of crop stress is one of the major applications of remote sensing in agriculture. Many researchers have confirmed the ability of remote sensing techniques for detection of pest/disease on cotton. Hence, this research was designed to investigate, (1) to study the spectral properties of noninfested and mealybug infested cotton crop (ii) damage assessment using remote sensing derived index. Mealybug-infested cotton crop showed significantly lower reflectance in the near infrared region and higher in the visible region of the spectrum when compared with the non-infested cotton crop. Meal

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Morgan, Penelope, Robert E. Keane, Gregory K. Dillon, et al. "Challenges of assessing fire and burn severity using field measures, remote sensing and modelling." International Journal of Wildland Fire 23, no. 8 (2014): 1045. http://dx.doi.org/10.1071/wf13058.

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Comprehensive assessment of ecological change after fires have burned forests and rangelands is important if we are to understand, predict and measure fire effects. We highlight the challenges in effective assessment of fire and burn severity in the field and using both remote sensing and simulation models. We draw on diverse recent research for guidance on assessing fire effects on vegetation and soil using field methods, remote sensing and models. We suggest that instead of collapsing many diverse, complex and interacting fire effects into a single severity index, the effects of fire should

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Klinger, Rob, Randy McKinley, and Matt Brooks. "An evaluation of remotely sensed indices for quantifying burn severity in arid ecoregions." International Journal of Wildland Fire 28, no. 12 (2019): 951. http://dx.doi.org/10.1071/wf19025.

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It is sometimes assumed the sparse and low statured vegetation in arid systems would limit the effectiveness of two remote-sensing derived indices of burn severity: the difference Normalised Burn Ratio (dNBR) and relativised difference Normalised Burn Ratio (RdNBR). We compared the relationship that dNBR, RdNBR and a ground-based index of burn severity (the Composite Burn Index, CBI) had with woody cover and woody density 1 year after burning in five fires that occurred in the Mojave Desert during 2005. Data were collected within 437 plots spanning geographic and elevation gradients representa

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Ergene, E. M., F. Bektaş Balçık, and F. Balik Şanlı. "TRENDS ANALYSIS OF AGRICULTURAL DROUGHT IN CENTRAL ANATOLIAN BASIN, TURKEY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (March 8, 2024): 141–48. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-141-2024.

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Abstract. Drought is a natural disaster that can have devastating economic, environmental, and social impacts on a global, national, and regional scale. Remote sensing technology is the most powerful tool to determine of drought categories and analyse drought severity, vulnerability and its trends. Because data that is temporal, quick, wide-area, and repeating can be obtained via remote sensing technologies. The calculation of drought indices with the use of remote sensing technology is crucial for assessing the severity of the drought and its evolution over time. In this study, the Central An

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Sawant, Sushant, and Shrikrishna Kukkemane. "Evaluating agricultural drought severity in Karnataka, India through integrated remote sensing indices." Ecocycles 10, no. 2 (2024): 128–43. https://doi.org/10.19040/ecocycles.v10i2.477.

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Agricultural drought significantly threatens Karnataka, India, where rain-fed agriculture is predominant. This study evaluates the spatio-temporal evolution of agricultural drought in Karnataka from 1986 to 2020 using the Advanced Drought Response Index (ADRI), which integrates the Vegetation Condition Index (VCI), Precipitation Condition Index (PCI), Soil Condition Index (SCI), and Temperature Condition Index (TCI). Data from NOAA, TerraClimate, and NASA POWER were processed using ArcGIS Pro. Key findings reveal that northern districts like Vijayapura, Chitradurga, and Gadag consistently expe

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Picotte, Joshua J., and Kevin M. Robertson. "Validation of remote sensing of burn severity in south-eastern US ecosystems." International Journal of Wildland Fire 20, no. 3 (2011): 453. http://dx.doi.org/10.1071/wf10013.

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We assessed an existing method of remote sensing of wildland fire burn severity for its applicability in south-eastern USA vegetation types. This method uses Landsat satellite imagery to calculate the Normalised Burn Ratio (NBR) of reflectance bands sensitive to fire effects, and the change in NBR from pre- to post fire (dNBR) to estimate burn severity. To ground-truth ranges of NBR and dNBR that correspond to levels of burn severity, we measured severity using the Composite Burn Index at 731 locations stratified by plant community type, season of measurement, and time since fire. Best-fit cur

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Nacheva, Lyubena. "Burn severity assessment of Rhodes island wildfire with remote sensing data." E3S Web of Conferences 638 (2025): 01011. https://doi.org/10.1051/e3sconf/202563801011.

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Forest fires are occurring more regularly and becoming more severe, which will cause significant environmental, economic, and social damage. The advanced information technologies, and especially Geographic Information Systems are contributing to dealing with this considerable world problem. This research aims to evaluate the damage and severity of the wildfire in the Greek island of Rhodes during July 2023. For this research, remote sensing images from the Landsat 8 Satellite Mission were used before and after the fire. It was calculated that the Normalized Burned Ratio index before and after

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Lei, Shuhan, Jianbiao Luo, Xiaojun Tao, and Zixuan Qiu. "Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors." Remote Sensing 13, no. 22 (2021): 4562. http://dx.doi.org/10.3390/rs13224562.

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). How

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Fok, Hok, and Qing He. "Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin." ISPRS International Journal of Geo-Information 7, no. 7 (2018): 286. http://dx.doi.org/10.3390/ijgi7070286.

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The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severit

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Sobrino, Jose, Rafael Llorens, Cristina Fernández, José Fernández-Alonso, and José Vega. "Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection." Forests 10, no. 5 (2019): 457. http://dx.doi.org/10.3390/f10050457.

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Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after

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Mutowo, Godfrey, and David Chikodzi. "Remote sensing based drought monitoring in Zimbabwe." Disaster Prevention and Management 23, no. 5 (2014): 649–59. http://dx.doi.org/10.1108/dpm-10-2013-0181.

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Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/appro

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Ebrahimi, Akhtar, Farhad Zolfaghari, Marzieh Ghodsi, and Fatemeh Narmashiri. "Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation." PLOS ONE 19, no. 7 (2024): e0305758. http://dx.doi.org/10.1371/journal.pone.0305758.

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Wind erosion resulting from soil degradation is a significant problem in Iran’s Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager’s (MSI) Level-1C satellite data was used to map Rutak’s degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on alb

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Vasilakou, Constantina, Dimitrios E. Tsesmelis, Kleomenis Kalogeropoulos, et al. "Assessing Drought Severity in Greece Using Geospatial Data and Environmental Indices." Geomatics 5, no. 1 (2025): 10. https://doi.org/10.3390/geomatics5010010.

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Drought represents a recurring natural event that holds notable socio-economic and environmental consequences. This research aims to analyze drought patterns in Greece by employing the standardized precipitation index (SPI) and several vegetation indices within a Geographic Information System (GIS) framework. GIS is a potent tool for integrating geospatial data, encompassing climatic, topographic, and hydrological information, enabling a comprehensive assessment of drought conditions. By examining historical precipitation data, the SPI quantifies the severity and duration of drought relative t

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Tian, Rong. "Spatiotemporal Analysis of Drought Characteristics Based on MODIS and GEE." Academic Journal of Science and Technology 13, no. 2 (2024): 227–32. http://dx.doi.org/10.54097/s1533h63.

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Drought is a major agricultural disaster that has long posed significant challenges to China's agriculture and ecological environment. Its latent, widespread, and vulnerability characteristics make it crucial to obtain effective large-scale, long-term remote sensing data for spatiotemporal analysis and drought forecasting. With the advancement of remote sensing technology, the spatiotemporal resolution of remote sensing data has improved, providing robust support for analyzing drought spatiotemporal variations. However, traditional remote sensing data analysis methods require extensive preproc

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Pla, M., A. Duane, and L. Brotons. "Potencial de las imágenes UAV como datos de verdad terreno para la clasificación de la severidad de quema de imágenes Landsat: aproximaciones a un producto útil para la gestión post incendio." Revista de Teledetección, no. 49 (December 5, 2017): 91. http://dx.doi.org/10.4995/raet.2017.7140.

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<p>Mapping fire severity is determinant to understand landscape evolution after a wildfire and provides useful information for decision making during post fire management. Quantitative fire severity mapping from relative changes in Normalized Burn Ratio index (RdNBR) is not actually being incorporated into decision making processes, being more useful the categorization in severity levels (high, moderate and low). However, the most common mapping severity methodologies based on the definition of RdNBR thresholds from field information are not always possible due to lack of field data or b

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Inocêncio, Tiago de M., Alfredo Ribeiro Neto, and Alzira G. S. S. Souza. "Soil moisture obtained through remote sensing to assess drought events." Revista Brasileira de Engenharia Agrícola e Ambiental 24, no. 9 (2020): 575–80. http://dx.doi.org/10.1590/1807-1929/agriambi.v24n9p575-580.

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ABSTRACT The sequence of drought events in the Northeast of Brazil in recent decades raises attention to the importance of studying this phenomenon. The objective of this study was to evaluate the duration and severity of drought events from 1988 to 2018 in hydrographic basins of the state of Pernambuco, Brazil, using two drought indexes: Standardized Soil Moisture Index and Soil Moisture Condition Index, calculated based on data of the Soil Moisture Project of the European Space Agency’s Climate Change Initiative. The duration of the droughts was determined considering the months between thei

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Zuzulova, Veronika, and Jaroslav Vido. "Normalized difference vegetation index (NDVI) as a tool for the evaluation of agricultural drought." Ecocycles 4, no. 1 (2018): 83–87. https://doi.org/10.19040/ecocycles.v4i1.124.

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The paper deals with the evaluation of agricultural drought in Slovakia by remote sensing data. For this purpose, monthly normalized difference vegetation index (NDVI) values were derived from MODIS spectroradiometer at district level during the time period of 2000 to 2014. Verification was based on correlations with the Palmer drought severity index<em> </em><em>(</em>PDSI) for twelve sites selected according to limited climatic parameters. Correlations showed a strong relationship (α ≤ 0.1) between NDVI and PDSI values, mainly in the middle of the growing season (June to September).

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Numata, Izaya, Mark A. Cochrane, and Lênio S. Galvão. "Analyzing the Impacts of Frequency and Severity of Forest Fire on the Recovery of Disturbed Forest using Landsat Time Series and EO-1 Hyperion in the Southern Brazilian Amazon." Earth Interactions 15, no. 13 (2011): 1–17. http://dx.doi.org/10.1175/2010ei372.1.

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Abstract Estimation of fire impacts and forest recovery using remote sensing is difficult because of the heterogeneity of fire history (frequency, severity, and time since last fire) across burned forest landscapes. The authors analyzed impacts of fire frequency and severity within recovering forests in the Amazon region using remote sensing. A multispectral Landsat time series dataset was used to reconstruct the fire history from 1990 to 2002 in a portion of Mato Grosso, Brazil. Five narrowband vegetation indices were then calculated from a hyperspectral Earth Observing One (EO-1) Hyperion im

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Argentiero, Ilenia, Giovanni Francesco Ricci, Mario Elia, et al. "Combining Methods to Estimate Post-Fire Soil Erosion Using Remote Sensing Data." Forests 12, no. 8 (2021): 1105. http://dx.doi.org/10.3390/f12081105.

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The increasing number of wildfires in southern Europe is making our ecosystem more vulnerable to water erosion; i.e., the loss of vegetation and subsequent runoff increase cause a shift in large quantities of sediment. Fire severity has been recognized as one of the most important parameters controlling the magnitude of post-fire soil erosion. In this paper, we adopted a combination of methods to easily assess post-fire erosion and prevent potential risk in subsequent rain events. The model presented is structured into three modules that were implemented in a GIS environment. The first module

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PANDYA, PARTHSARTHI A., and NARENDRA KUMAR GONTIA. "Improving remote sensing based agricultural drought characterization in Saurashtra, Gujarat : A region-specific threshold approach." MAUSAM 75, no. 2 (2024): 381–94. http://dx.doi.org/10.54302/mausam.v75i2.6077.

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Remote sensing technology has demonstrated its significant utility in the monitoring and mapping of agricultural drought on a global scale. This study focused on the assessment of agricultural drought in the Saurashtra region of Gujarat, India, utilizing a comprehensive dataset spanning 33 years from Landsat and Sentinel satellites. It employed various vegetation indices, including NDVI (Normalized Difference Vegetation Index), Anomaly Index (NAI), Vegetation Condition Index (VCI) and NDWI Anomaly index (NDWIA), to gauge drought conditions. The performance of these indices was evaluated throug

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Hall, R. J., J. T. Freeburn, W. J. de Groot, J. M. Pritchard, T. J. Lynham, and R. Landry. "Remote sensing of burn severity: experience from western Canada boreal fires." International Journal of Wildland Fire 17, no. 4 (2008): 476. http://dx.doi.org/10.1071/wf08013.

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The severity of a burn for post-fire ecological effects has been assessed with the composite burn index (CBI) and the differenced Normalized Burn Ratio (dNBR). This study assessed the relationship between these two variables across recently burned areas located in the western Canadian boreal, a region not extensively evaluated in previous studies. Of particular interest was to evaluate the nature of the CBI–dNBR relationship from the perspectives of modelling, the influence of fire behaviour prediction (FBP) fuel type, and how field observations could be incorporated into the burn severity map

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Shinde, Prakash S., and Namdev V. Telore. "Drought Risk Assessment in the Yerla River Basin of India using Remote Sensing and GIS Methods." Disaster Advances 18, no. 4 (2025): 30–36. https://doi.org/10.25303/184da030036.

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This study provides a comprehensive evaluation of agricultural drought in the Yerla River Basin of India, leveraging satellite-based remote sensing data and advanced drought indices. Key metrics, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Moisture Stress Index (MSI), Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI), were employed to assess drought severity over a 30-years period (1994–2023). Findings indicate that the northern and central regions of the basin are particularly

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Omidvar, Kamal, Masoume Nabavizadeh, Iman Rousta, and Haraldur Olafsson. "Remote Sensing-Based Drought Monitoring in Iran’s Sistan and Balouchestan Province." Atmosphere 15, no. 10 (2024): 1211. http://dx.doi.org/10.3390/atmos15101211.

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Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought

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Gerardo, Romeu, and Isabel P. de Lima. "Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)." Water 14, no. 15 (2022): 2284. http://dx.doi.org/10.3390/w14152284.

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Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is oft

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Gale, Matthew G., and Geoffrey J. Cary. "What determines variation in remotely sensed fire severity? Consideration of remote sensing limitations and confounding factors." International Journal of Wildland Fire 31, no. 3 (2022): 291–305. http://dx.doi.org/10.1071/wf21131.

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Analyses of the effects of topography, weather, land management, and fuel on fire severity are increasingly common, and generally apply fire severity indices derived from satellite optical remote sensing. However, these indices are commonly interpreted with insufficient appreciation for their limitations and may be inappropriately invoked as representing physical fire effects and fire behaviour. For a large wildfire in southeast Australia, we investigated three considerations for inferring robust insights from fire severity analyses – the potentially confounding influences of pre-fire vegetati

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Youb, Okkacha, Meriem Boultif, Said Bourfa, et al. "Climatic sensitivity in the Algerian steppes: Drought indices and remote sensing." Technium Social Sciences Journal 31 (May 9, 2022): 810–17. http://dx.doi.org/10.47577/tssj.v31i1.6443.

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These last years, the Algerian steppe has known a strong degradation caused in particular by the scarcity of the annual rainfall. It spreads in certain cases to successive years generated by a persistent drought. A series of indices were applied to rainfall records recorded for 30 years (1985-2015) in the treated stations to identify the severity of the drought in the steppe regions. The present work proposes to study and compare the performance of some drought indices to identify a permanent monitoring system in the steppe. The indices studied are Pluviometric Deficit Index (PDI), StandaPDIze

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Leandro, Esteban Rodriguez, Muditha K. Heenkenda, and Kerin F. Romero. "Estimating Sugarcane Maturity Using High Spatial Resolution Remote Sensing Images." Crops 4, no. 3 (2024): 333–47. http://dx.doi.org/10.3390/crops4030024.

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Sugarcane suffers from the increased frequency and severity of droughts and floods, negatively affecting growing conditions. Climate change has affected cultivation, and the growth dynamics have changed over the years. The identification of the development stages of sugarcane is necessary to reduce its vulnerability. Traditional methods are inefficient when detecting those changes, especially when estimating sugarcane maturity—a critical step in sugarcane production. Hence, the study aimed to develop a cost- and time-effective method to estimate sugarcane maturity using high spatial-resolution

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Tureniyazova, Asiya, Timur Berdimbetov, and Karimullaeva Ayzada. "Using GIS And Remote Sensing inAssessing theImpact ofDrought onForest Cover Changes Around theAral Sea." American Journal of Applied Science and Technology 5, no. 5 (2025): 12–15. https://doi.org/10.37547/ajast/volume05issue05-04.

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The Aral Sea region has experienced significant ecological changes due to prolonged droughts and unsustainable water use, resulting in the degradation of forest ecosystems. This study utilizes Geographic Information Systems (GIS) and remote sensing techniques to analyze the impact of drought on forest cover changes from 2000 to 2024. Using satellite imagery (Landsat and Sentinel-2) and climate indicators such as the Standardized Precipitation Index (SPI), temporal and spatial trends in vegetation health and forest loss were assessed. The findings reveal a strong correlation between increasing

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Han, Ge, Wei Gong, Xiaohui Cui, Miao Zhang, and Jun Chen. "ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 73–77. http://dx.doi.org/10.5194/isprs-archives-xli-b8-73-2016.

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The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference ve

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Han, Ge, Wei Gong, Xiaohui Cui, Miao Zhang, and Jun Chen. "ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 73–77. http://dx.doi.org/10.5194/isprsarchives-xli-b8-73-2016.

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The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference ve

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Dr., M. Sakthivel*1 Dr. M. H. Kalubarme2 Panhalkar Bilal Dastagir 3. &. Gaikwad Vishal Vilas4. "AGRICULTURAL DROUGHT MONITORING USING GEO-INFORMATICS TECHNOLOGY: A CASE STUDY OF SURENDRANAGAR DISTRICT, GUJARAT." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 11 (2018): 126–36. https://doi.org/10.5281/zenodo.1493912.

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Drought is the most complex but least understood of all natural hazards. It is broadly defined as “sever water shortage”. In recent years, Geographic Information System (GIS) and Remote Sensing (RS) have played a key role in studying different types if hazards either natural or man-made. This study stresses upon the use of RS and GIS in the field of Drought Risk assessment. Various drought indices were computed using Landsat 7 data of February 2000, Landsat 4-5 TM data of February 2009 and Landsat-8 OLI & TIRS data of February 2018 as well as meteorological data for drought sev

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Utami, Rahma Kurnia Sri, Zulkarnain Zulkarnain, Sudarmi Sudarmi, Listumbinang Halengkara, and Farah Azzahra Rahian. "Drought zone monitoring with remote sensing technology in Metro City, Indonesia." Journal of Environment and Geography Education 1, no. 1 (2024): 24–40. http://dx.doi.org/10.61511/jegeo.v1i1.2024.685.

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Background: Lampung Province, Indonesia, is prone to drought, with 232 villages experiencing drought in recent years according to data from the Central Statistics Agency (BPS). Metro City, a region within Lampung Province, is particularly susceptible to drought, as evidenced by a decrease in agricultural production due to drought conditions observed during a three-month period (December 2021–February 2022). Despite the agricultural sector being a crucial economic driver in the region, drought poses significant challenges. Remote Sensing and Geographic Information Systems (GIS) technologies off

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Novo, A., H. González-Jorge, J. Martínez-Sánchez, and H. Lorenzo. "REMOTE SENSING APPROACH TO EVALUATE POST-FIRE VEGETATION STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1031–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1031-2020.

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Abstract. Spain is included in the top five European countries with the highest number of wildfires. Forest fire can produce significant impacts on the structure and functioning of natural ecosystems. After a forest fire, the evaluation of the damage severity and spatial patterns are important for forest recovery planning, which plays a critical role in the sustainability of the forest ecosystem. The process of forest recovery and the ecological and physiological functions of the burned forest area should be continuously monitored. Remote sensing technologies and in special LiDAR are useful to

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Yao, Peiwen, Hong Fan, and Qilong Wu. "Optimal Drought Index Selection for Soil Moisture Monitoring at Multiple Depths in China’s Agricultural Regions." Agriculture 15, no. 4 (2025): 423. https://doi.org/10.3390/agriculture15040423.

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Droughts are a major driver of global environmental degradation, threatening lives and causing significant economic losses, with approximately 80% of these losses linked to agricultural drought, characterized by soil moisture deficits. Remote sensing technology offers high spatiotemporal resolution data for continuous monitoring of soil moisture and drought severity. However, the effectiveness of remote sensing drought indices across different soil depths remains unclear. This study assessed the performance of eight widely used drought indices—Perpendicular Drought Index (PDI), Modified Perpen

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Ngoc Hanh, Le, Nguyen Hoang Son, Le Phuc Chi Lang, Tran Thi An, Nguyen Van An, and Truong Phuoc Minh. "Spatial-Temporal Assessment of Drought in Hoa Vang district, Da Nang City, Vietnam Using Remote Sensing and Google Earth Engine." IOP Conference Series: Earth and Environmental Science 1345, no. 1 (2024): 012013. http://dx.doi.org/10.1088/1755-1315/1345/1/012013.

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Abstract The objective of this research was to map the drought risk in Hoa Vang district, Da Nang city, and analyze the variability of drought events from 1991 to 2020 using remote sensing imagery. To achieve this, remote sensing and GIS methods were utilized to gather information on land surface temperature and the Normalized Difference Vegetation Index (NDVI). The study effectively employed the Google Earth Engine which is a cloud-based computing platform to analyze image sequences from Landsat 5 and OLI sensors spanning the period from 1991 to 2020. This analysis aimed to extract the remote

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Marcos, Elena, Víctor Fernández-García, Alfonso Fernández-Manso, et al. "Evaluation of Composite Burn Index and Land Surface Temperature for Assessing Soil Burn Severity in Mediterranean Fire-Prone Pine Ecosystems." Forests 9, no. 8 (2018): 494. http://dx.doi.org/10.3390/f9080494.

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We analysed the relationship between burn severity indicators, from remote sensing and field observations, and soil properties after a wildfire in a fire-prone Mediterranean ecosystem. Our study area was a large wildfire in a Pinus pinaster forest. Burn severity from remote sensing was identified by studying immediate post-fire Land Surface Temperature (LST). We also evaluated burn severity in the field applying the Composite Burn Index (CBI) in a total of 84 plots (30 m diameter). In each plot we evaluated litter consumption, ash colour and char depth as visual indicators. We collected soil s

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Sánchez Sánchez, Yolanda, Antonio Martínez Graña, and Fernando Santos- Francés. "Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas." Agronomy 11, no. 8 (2021): 1459. http://dx.doi.org/10.3390/agronomy11081459.

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Soil erosion is one of the most important environmental problems of the moment, especially in areas affected by wildfires. In this paper, we study pre-fire and post-fire erosion using remote sensing techniques with Sentinel-2 satellite images and LiDAR. The Normalized Burn Ratio is used to determine the areas affected by the fire that occurred on 18 August 2016 in the Natural Reserve of Garganta de los Infiernos (Cáceres). To calculate the erosion, the multi-criteria analysis is carried out from the RUSLE. Once all calculations were performed, there was a considerable increase in sediment prod

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Zikiou, Nadia, Holly Rushmeier, Manuel I. Capel, Tarek Kandakji, Nelson Rios, and Mourad Lahdir. "Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria." Remote Sensing 16, no. 9 (2024): 1517. http://dx.doi.org/10.3390/rs16091517.

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Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques,

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Zahabnazouri, Somayeh, Patrick Belmont, Scott David, Peter E. Wigand, Mario Elia, and Domenico Capolongo. "Detecting Burn Severity and Vegetation Recovery After Fire Using dNBR and dNDVI Indices: Insight from the Bosco Difesa Grande, Gravina in Southern Italy." Sensors 25, no. 10 (2025): 3097. https://doi.org/10.3390/s25103097.

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Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and

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Lee, Kangsan, Willem J. D. van Leeuwen, Jeffrey K. Gillan, and Donald A. Falk. "Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms." Remote Sensing 16, no. 10 (2024): 1803. http://dx.doi.org/10.3390/rs16101803.

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Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). St

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Morcrette, Cyril, Katie Brown, Rebecca Bowyer, Philip Gill, and Dan Suri. "Development and Evaluation of In-Flight Icing Index Forecast for Aviation." Weather and Forecasting 34, no. 3 (2019): 731–50. http://dx.doi.org/10.1175/waf-d-18-0177.1.

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Abstract An in-flight icing index from the literature is implemented in the Met Office forecasting system. Comparison of hindcasts of cloud fraction with ground-based remote sensing observations of liquid and ice cloud fraction are used to inform a reformulation of part of the index. Satellite-retrieved icing potential is then used to quantitatively assess the reliability and skill of the new index and compare its performance to the current operational one. Having shown that the new index has substantially better reliability, ways of presenting separate icing likelihood and severity are explor

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Sun, Xiaofang, Meng Wang, Guicai Li, and Yuanyuan Wang. "Regional-scale drought monitor using synthesized index based on remote sensing in northeast China." Open Geosciences 12, no. 1 (2020): 163–73. http://dx.doi.org/10.1515/geo-2020-0037.

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AbstractDrought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) d

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Mbuh, Mbongowo Joseph. "Revisiting the Impact of Drought Over Mid-Continent USA From 2000-2019 Using High Resolution Data." International Journal of Applied Geospatial Research 13, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijagr.298301.

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Drought-associated water shortage a complicated hazard, and extreme weather and climate events have serious impacts on agricultural, ecological, and socio-economic activities in any society. This work focuses on drought analysis using a high-resolution remote sensing and meteorological dataset from MODIS’s NDVI and LST to evaluate the Spatio-temporal distribution drought events and intensities in three physiographic regions of the USA from 2000–2019. Results were compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), Vegetation Health index (VH

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PATIL, PRITAM, M. P. JAGTAP, and K. K. DAKHORE. "Drought severity estimation using NDWI index in Parbhani district of Maharashtra." Journal of Agrometeorology 26, no. 2 (2024): 225–27. http://dx.doi.org/10.54386/jam.v26i2.2540.

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The study was carried out to investigate the impact of drought on vegetation in Maharashtra's Parbhani district, utilizing remote sensing techniques. Analysis of Landsat 8 data for 2015 (a drought year) and 2020 (a normal year) reveals fluctuations in the Normalized Difference Water Index (NDWI) closely correlated with rainfall patterns. In 2015, NDWI indicated extreme drought conditions, while in 2020, most areas experienced mild drought. The comparison underscores NDWI's sensitivity to rainfall variability and dry spells. Meteorological factors, geographical features, and human activities in

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Liu, Pengfei, Weiyu Zhuang, Weili Kou, Leiguang Wang, Qiuhua Wang, and Zhongjian Deng. "Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions." Forests 16, no. 2 (2025): 263. https://doi.org/10.3390/f16020263.

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Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with

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Morante-Carballo, Fernando, Lady Bravo-Montero, Paúl Carrión-Mero, Andrés Velastegui-Montoya, and Edgar Berrezueta. "Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador." Remote Sensing 14, no. 8 (2022): 1783. http://dx.doi.org/10.3390/rs14081783.

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Worldwide, forest fires exert effects on natural ecosystems, contributing to economic/human losses, health effects, and climate change. Spectral indices are an essential tool for monitoring and analyzing forest fires. These indices make it possible to evaluate the affected areas and help mitigate possible future events and reduce damage. The case study addressed in this work corresponds to the Cerro of the Guadual community of La Carolina parish (Ibarra, Ecuador). This work aims to evaluate the degree of severity and the recovery of post-fire vegetation, employing the multitemporal analysis of

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Russell-Smith, Jeremy, and Andrew C. Edwards. "Seasonality and fire severity in savanna landscapes of monsoonal northern Australia." International Journal of Wildland Fire 15, no. 4 (2006): 541. http://dx.doi.org/10.1071/wf05111.

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Ten years of photo and associated data records from an extensive fire and vegetation effects monitoring programme established in two large north Australian National Parks were used to (1) develop a simple-to-use semiquantitative fire severity index based on observed fire impact on vegetation, particularly leaf-scorch height; and (2) explore relationships between seasonality and fire severity in different landform and vegetation types. Using a three-tiered fire severity scale, data for 719 fires recorded from 178 plots over the period 1995–2004 indicate that the great majority of early dry seas

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Wei, Wei, Jiping Wang, Libang Ma, et al. "Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data." Land 13, no. 1 (2024): 95. http://dx.doi.org/10.3390/land13010095.

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Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-e

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Baik, Jongjin, Jongmin Park, and Minha Choi. "Estimation and Assessment of Total Phosphorus and Trophic State Index Estimated from Landsat 8 Dataset on 2018 at Daecheong Reservoir." Journal of Korean Society of Environmental Engineers 42, no. 9 (2020): 415–23. http://dx.doi.org/10.4491/ksee.2020.42.9.415.

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Objectives : Remote sensing of water quality parameters (WQPs) has been widely applied along with the emerging importance of obtaining the spatial pattern of water quality at inland reservoir. This study retrieved the Total Phosphorus (TP) via remote sensing imagery at Daecheong reservoir and used it for acquiring the spatial distribution of Trophic State Index (TSI).Methods : First, stepwise regression for TP was established using the ground-based TP measurement and reflectance of Landsat 8 collocated in space and time. With the developed regression model, we estimated the spatial distributio

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García-Llamas, Paula, Susana Suárez-Seoane, Angela Taboada, et al. "Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: a case study on a large fire in NW Spain." International Journal of Wildland Fire 28, no. 7 (2019): 512. http://dx.doi.org/10.1071/wf18156.

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This study analyses the suitability of remote sensing data from different sources (Landsat 7 ETM+, MODIS and Meteosat) in evaluating the effect of fuel conditions on fire severity, using a megafire (11891ha) that occurred in a Mediterranean pine forest ecosystem (NW Spain) between 19 and 22August 2012. Fire severity was measured via the delta Normalized Burn Ratio index. Fuel conditions were evaluated through biophysical variables of: (i) the Visible Atmospherically Resistant Index and mean actual evapotranspiration, as proxies of potential live fuel amount; and (ii) Land Surface Temperature a

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