A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: April 8, 2023

Abstract Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led a high demand for catalogues and tools their computation. However, most these resources either closed-source, outdated, unconnected catalogue or lacking common Application Programming Interface (API). Here we present “Awesome Indices” (ASI), standardized research. ASI provides comprehensive machine readable indices, which is linked Python library. delivers broad set attributes each index, including names, formulas, source references. can be extended by the user community, ensuring that remains current enabling wider range scientific applications. Furthermore, library enables application real-world data thereby facilitates efficient use in multiple domains.

Language: Английский

---

U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 869, P. 161757 - 161757

Published: Jan. 21, 2023

Language: Английский

---

Monitoring inland water via Sentinel satellite constellation: A review and perspective

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 204, P. 340 - 361

Published: Sept. 28, 2023

Language: Английский

---

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242

Published: June 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Language: Английский

---

Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(2), P. 893 - 956

Published: Feb. 12, 2024

Language: Английский

---

Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(6), P. 1678 - 1678

Published: March 20, 2023

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies background, indices are widely used in body extraction. Whether one index its optimal threshold can maintain best all year round is a question. At present, however, little research has considered effect time or conducted experiments with data different months. To identify for extraction, two regions Jilin Province were selected case study comprehensive comparative analysis considering acquisition was conducted. Ten calculated based on 30 m spatial resolution TM/OLI acquired 1999 2001 2019 2021 May to October. The included Modified Normalized Difference Water Index (NDWI3 MNDWI), Automated Extraction (AWEI) images without shadow, Multi-Band (MBWI), New (NWI), Ratio (WRI), Sentinel-2 (SWI) originally imagery, Comprehensive (NCIWI), Surfaces (IWS), Enhanced (EWI). OTSU algorism adopted adaptively determine segmentation each compared terms inter-class separability, sensitivity, robustness, extraction accuracy. result showed that NWI EWI performed months years, enhancement could suppress background information, especially water-related land use types cloud pollution. Their thresholds throughout period more stable than others, ranges −0.342 −0.038 −0.539 −0.223, respectively. Based thresholds, they achieved overall accuracies 0.952 0.981 0.964 0.981, commission errors 0 28.2% 7.7%, omission 15% 8%, kappa coefficient above 0.8 indicating good results. results demonstrated effectiveness combined algorithm better monitoring during periods offering reliable Even though this only focuses lakes within regions, have potential accurately over other regions.

Language: Английский

---

Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study

Water, Journal Year: 2022, Volume and Issue: 14(3), P. 299 - 299

Published: Jan. 19, 2022

Mapping watercourses and their surroundings through remote sensing methods is a fast, continuous, effective method crucial tool for capturing change possibly predicting hazards. Thanks to Synthetic Aperture Radar (SAR) technology the ability of its backscattered emitted radiation penetrate atmosphere under any conditions, this type mapping water surfaces particular importance. This paper presents possibility using SAR long-term observations changes in behaviour rivers river systems, combined with optical multispectral images Sentinel-2. Additionally, it aims demonstrate suitability satellite data implementation watercourses, caused not only by natural development but especially inundation processes catchment area. Appropriate Sentinel-1 image processing evaluation procedures that usage vertical-vertical (VV) polarisation configuration suitable methodology documenting bodies, Lee filter an acceptable radar noise filtering. The extraction process based on determination threshold values “Otsu” principle. Subsequently, comparison results realised spectral indices water—the Normalized Difference Water Index (NDWI), Modified (MNDWI), pair Automated Extraction (AWEI) indices, supervised classification Maximum Likelihood Classification (MLC). are numerical graphical evaluated. In assessing accuracy extraction, highest achieved Overall Accuracy (OA) were maximum 98.6%. On average, lower User (UA) 93.1%, where VV also dominates. However, vertical-horizontal (VH) dominates Producer (PA) 84.9%.

Language: Английский

---

Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring 2021

Hydrology, Journal Year: 2022, Volume and Issue: 9(8), P. 135 - 135

Published: July 28, 2022

Global warming together with environmental pollution threatens marine habitats and causes an increasing number of disasters. Periodic monitoring coastal water quality is critical importance for the effective management resources sustainability ecosystems. The use remote sensing technologies provides significant benefits detecting, monitoring, analyzing rapidly occurring displaced natural phenomena, including mucilage events. In this study, five indices estimated from cloud-free partly cloudy Sentinel-2 images acquired May to July 2021 were employed effectively map aggregates on sea surface in Izmit Bay using cloud-based Google Earth Engine (GEE) platform. Results showed that started coverage about 6 km² 14 May, reached highest level 24 diminished at end July. Among applied indices, Adjusted Floating Algae Index (AFAI) was superior producing maps even image, followed by Normalized Difference Turbidity (NDTI) Mucilage (MI). To be more specific, green channel found inferior extracting information satellite images.

Language: Английский

---

A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution

Water, Journal Year: 2022, Volume and Issue: 14(17), P. 2696 - 2696

Published: Aug. 30, 2022

Satellite-based remote sensing is important for monitoring the spatial distribution of water resources. The index currently one most widely used body extraction methods. Based on Sentinel-2 image, this study combines area-to-point regression kriging interpolation, bilinear and Gram–Schmidt (GS) pan-sharpening method with indices MNDWI, AWEIsh WI2015 to compare different experimental results showed that all have satisfactory ability, kappa coefficient as an accuracy threshold above 0.8. Moreover, GS downscaling combined yielded best performance. This research demonstrates efficacy extract bodies in urban areas its ability comprehensively describe river bodies. findings indicate high-resolution band information particularly improving low-resolution can significantly minimize erroneous extraction.

Language: Английский

---

Automated mucilage extraction index (AMEI): a novel spectral water index for identifying marine mucilage formations from Sentinel-2 imagery

International Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 44(1), P. 105 - 141

Published: Jan. 2, 2023

ABSTRACTABSTRACT Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and pollution. Monitoring sea surface formations mapping their spatial distributions provide valuable information to local authorities decision-makers in developing prevention rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) allows effective accurate detection aggregates using Sentinel-2 satellite imagery. The uses four bands Level-2A imagery (Bands 3, 4, 8, 12) covering visible, near-infrared, shortwave infrared regions. was formulated considering image acquired on 19 May 2021, when were most intensively observed Sea Marmara. performance developed then evaluated images 14 24 13 June 2021. Jenks Natural Breaks (JNB) algorithm applied estimate threshold values for separating water background classify maps into two classes: ‘mucilage’ ‘others’. To statistically analyse effectiveness indices distinguishing formations, proposed compared with 21 widely used by applying Bhattacharyya Distance, Jeffries-Matusita M-Statistic measures. Results confirm robustness index, offering superior separation (above 1.5 terms M-Statistic) other both cloud-free cumulus clouds. Visual interpretations also verified boundaries cloudless thin cloudy accurately identified different types (i.e. yellow white) can be an appropriate histogram thresholding applied.KEYWORDS: Sentinel-2marine mucilagesea snotspectral indexwater AcknowledgmentWe would like thank European Space Agency providing research.Disclosure statementThe authors declare they have no known competing financial interests or personal relationships could appeared influence work reported this paper.Data availability data support findings are available request corresponding author ([email protected]).

Language: Английский

---