Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2023, Volume and Issue: 16, P. 1483 - 1502

Published: Jan. 1, 2023

Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought evaluating its spatiotemporal dynamics are essential to manage regional conditions protecting environment. In this study, various single remote sensing-based indices including soil moisture condition index (SMCI), precipitation (PCI), temperature (TCI), vegetation (VCI) combined RS-based Indices optimized meteorological (OMDI) synthesized (SDI) have used investigate variations agricultural droughts between 2000 2021 in Iran. The in situ indices, standardized (SPI) evapotranspiration (SPEI) series 1, 3, 6, 12, 24 months were utilized verify evaluate their applicability for analyzing conditions. results indicated that correlation coefficients with higher than indexes. Generally, single-factor indexes, VCI, TCI, PCI, SMCI, specific characteristics. PCI SMCI an acceptable short-term SPI SPEI more applicable Further, TCI better performance long-term This research concluded central, eastern, southeastern parts Iran mainly experiencing exceptional extreme worst observed years 2008 region during last 20 years. also showed that, 2019 2020, most areas had OMDI SDI values severity decreased these Particularly, provides reference reasonably choosing from a local global scale.

Language: Английский

---

A review of recent developments on drought characterization, propagation, and influential factors

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 898, P. 165550 - 165550

Published: July 17, 2023

Language: Английский

---

Enhancing Drought Forecast Accuracy Through Informer Model Optimization

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 126 - 126

Published: Jan. 9, 2025

As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed water resource management. This study employed Informer model forecast conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), Convolutional Neural Network (CNN) models. The findings indicate that outperforms other three models terms forecasting across all time scales. Nevertheless, predictive capacity remains suboptimal when it comes intervals. Aiming at problem short scale, this proposed named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) JAVA optimization algorithm improve model. VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, performance these was evaluated using root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (MAE). model’s 1-month SPEI significantly surpasses alternative demonstrates robust agreement actual data. Simultaneously, exhibits equally optimal different In order validate model, four meteorological stations Songliao River Basin were chosen random. validation results demonstrate scale (NSE values 0.8663, 0.8765, 0.8822, 0.8416, respectively). Additionally, scales, further demonstrating its generalizability excellence shorter

Language: Английский

---

Measuring pedestrian-level street greenery visibility through space syntax and crowdsourced imagery: A case study in London, UK

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128725 - 128725

Published: Feb. 1, 2025

Language: Английский

---

Propagation from meteorological to hydrological drought and its application to drought prediction in the Xijiang River basin, South China

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 617, P. 128889 - 128889

Published: Dec. 14, 2022

Language: Английский

---

Drought analysis using normalized difference vegetation index and land surface temperature over Niamey region, the southwestern of the Niger between 2013 and 2019

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 52, P. 101689 - 101689

Published: Feb. 10, 2024

Semi-arid regions are highly susceptible to drought due their low annual precipitation and ecological vulnerability climate change. This study focuses on the Niamey region in southwestern Niger employs Vegetation Health Index (VHI) assess severity its changes. Using data from Landsat 8 OLI/TIRS, including Normalized Difference (NDVI) Land Surface Temperature (LST), we derived Condition (VCI), (TCI), for 2013 2019. Analysis of time series 2019 reveals that experienced severe drought, with 62.31 km2 42.35 km2, respectively, facing a lack precipitation. Notably, extreme droughts covered large area 55.75 accounting 13.94 % region, indicating an increase frequency Furthermore, NDVI values ranged 0.50 − 0.18, while those 0.57 0.20. Additionally, relationship between LST appeared be linear inversely proportional both (R2 = 0.34, P 0.58) 0.06, 0.25). Rising demonstrated significant effects plants, surface features playing crucial role. The significance this research is understand has impact agriculture, water resources development. Remote sensing monitor high resolution over areas showed patterns distribution during period Niamey. could provide valuable insights into land environmental planning tropical regions.

Language: Английский

---

Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102717 - 102717

Published: July 6, 2024

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe complex. The monitoring is crucial for reducing drought-related losses ensuring national ecological security. This study used coupled PROSPECT SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as growth stages varying conditions. Our objective was reveal spectral response characteristics grasslands conditions identify sensitive bands suitable during different stages. We aligned commonly available satellite from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView Gaofen 2 (GF 2) with these assess capabilities existing data monitoring. Furthermore, this research evaluated suitability 16 remote sensing vegetation indices monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Ratio (RVI), (DVI), Modified Soil Adjusted (MSAVI), Atmospherically Resistant (ARVI), Water (MNDWI), Global Moisture (GVMI), Land Surface (LSWI), Visible Shortwave Infrared Drought (VSDI), index(WI), Stress Index(MSI), Index(NDWI), (NDII), Photochemical Reflectance (PRI), Optimized Soil-Adjusted (OSAVI). simulation analysis results revealed: 1) Grassland in exhibit similar sensitivities certain bands, namely those within ranges 540 nm–720 nm, 1250 nm–1690 1805 nm–2190 2264 nm–2500 which are more various Suitable both growing stable include NDII, MSI, PRI, LSWI, GVMI, silhouette coefficients exceeding 0.6 stage 0.7 stage. least index DVI, an average coefficient 0.15 over entire 3) From band perspective, among five assessed satellites, MODIS Band 7 exhibits highest sensitivity water content across all bands. MODIS's configuration most stages, while 2's suitable.

Language: Английский

---

Deciphering the relationship between meteorological and hydrological drought in Ben Tre province, Vietnam

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(6), P. 5869 - 5894

Published: Feb. 22, 2024

Language: Английский

---

Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(6), P. 4251 - 4288

Published: March 5, 2024

Language: Английский

---

Comprehensive Assessment of Drought Susceptibility Using Predictive Modeling, Climate Change Projections, and Land Use Dynamics for Sustainable Management

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 337 - 337

Published: Feb. 7, 2025

This study assessed the drought susceptibility in Golestan Province, Northeastern Iran, using land use change modeling and climate projections from CMIP6 framework, under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) for 2030–2050. The development of current (2022) future maps was based on agrometeorological sample points 14 environmental factors—such as use, precipitation, mean temperature, soil moisture, remote sensing-driven vegetation indices—used inputs into a machine learning model, maximum entropy. model showed very robust predictive capacity, with AUCs training test data 0.929 0.910, thus certifying model’s reliability. analysis identified major hotspots Gomishan Aqqala, where 66.12% 36.12% their areas, respectively, exhibited “very high” susceptibility. Projections SSP scenarios, particularly SSP5-8.5, indicate that risk will be most severe Maraveh Tappeh, 72.09% area exhibits risk. results revealed Province is at crossroads. Rising temperatures, exceeding 35 °C summer, combined declining rainfall, intensify agricultural hydrological droughts. These aggravated risks are compounded transitions rangelands to bare land, mostly Aqqala Gomishan, besides urban expansion Bandar-e Torkman Bandar Gaz, all which face less groundwater recharge increased surface runoff. Golestan’s vulnerability has both local regional impacts, its affecting neighboring communities ecosystems. Trade, migration, ecological stresses linked water resources may emerge critical challenges, requiring collaboration mitigation. Targeted interventions prioritizing sustainable practices, cooperation, collaborative strategies essential address mitigate these cascading safeguard vulnerable communities.

Language: Английский

---