Improved method for cropland extraction of seasonal crops from multi-sensor satellite data DOI
Danish Raza, Hong Shu, Majid Nazeer

и другие.

International Journal of Remote Sensing, Год журнала: 2024, Номер 45(18), С. 6249 - 6284

Опубликована: Авг. 26, 2024

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

Язык: Английский

Prediction of spatial-temporal flood water level in agricultural fields using advanced machine learning and deep learning approaches DOI

Adisa Hammed Akinsoji,

Bashir Adelodun, Qudus Adeyi

и другие.

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Язык: Английский

Процитировано

2
Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance DOI

G. Shankar,

M. Kalaiselvi Geetha,

P. Ezhumalai

и другие.

SN Computer Science, Год журнала: 2025, Номер 6(3)

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0
Analysis and visualization of spatio-temporal variations of ecological vulnerability in Pakistan using satellite observation datasets DOI Creative Commons
Muhammad Kamran,

Kayoko Yamamoto

Environmental and Sustainability Indicators, Год журнала: 2024, Номер 23, С. 100425 - 100425

Опубликована: Июнь 20, 2024

Pakistan is the fifth most populous country in world. Its ecological environment facing numerous stresses such as climate change, rapid urbanization, natural disasters, and a decline air quality. Thus, scientific understanding of spatial temporal changes Pakistan's crucial for formulating an informed strategy regional sustainability. This study used Google Earth Engine platform Remote Sensing Ecological Index (RSEI) to investigate vulnerability three provinces 1990, 2000, 2010, 2020. Landsat 5 8 datasets are construct RSEI indicators Principal Component Analysis (PCA) adopted objectively compute past decades. The results indicated that (1) Punjab province exhibited slightly improved trend from 1990 2020 with overall dominance 'moderate' level all four years; (2) Sindh has declining 'poor' contributing 28.6% total area compared 1.04% 1990; (3) Balochistan shown resilience some extent during 1900-2010 vulnerability. However, observed between 2010 These research can provide support Punjab, Sindh, achieving sustainable development while conserving environment.

Язык: Английский

Процитировано

2
Improved method for cropland extraction of seasonal crops from multi-sensor satellite data DOI
Danish Raza, Hong Shu, Majid Nazeer

и другие.

International Journal of Remote Sensing, Год журнала: 2024, Номер 45(18), С. 6249 - 6284

Опубликована: Авг. 26, 2024

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

Язык: Английский

Процитировано

1