An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth DOI Creative Commons

Zhenxian Xu,

Xin Shen,

Sang Ge

и другие.

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Июль 18, 2024

Introduction As an exceptional geographical entity, the vegetation of Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in south-eastern sector QTP, serving as a transition area from sub-tropical evergreen broadleaf forest high-mountain vegetation. However, there has been limited exploration into predicting temporal and spatial variability cover using anti-interference methods address outliers long-term historical data. Additionally, correlation between these variables environmental factors natural forests with complex terrain rarely analyzed. Methods This study developed advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional cover) accurately evaluate predict time shifts FVC within BNNR, utilizing GEE (Google Earth Engine). satellite data utilized this paper consisted Landsat images spanning 1986 to2020. By integrating methodologies monitor assess trend, Hurst index was employed forecast FVC. Furthermore, association topographic evaluated, partial climatic influences analyzed at pixel level (30×30m). Results discussion Here are results research: (1) Overall, BNNR growth mean value increasing 59.40% 68.67% 2020. (2) TS-MK algorithm showed that percentage decreasing trend 59.03% (significant increase 28.04%) 22.13% decrease 6.42%), respectively. coupling exponent Theil-Sen estimator suggests majority regions projected sustain upward future. (3) Overlaying outcomes revealed changes were notably influenced by elevation. analysis indicated temperature exerts significant influence cover, demonstrating correlation.

Язык: Английский

Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India DOI
Rajkumar Guria, Manoranjan Mishra,

Samiksha Mohanta

и другие.

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

1
Forecasting shoreline dynamics and land use/land cover changes in Balukhand-Konark Wildlife Sanctuary (India) using geospatial techniques and machine learning DOI
Manoranjan Mishra, Debdeep Bhattacharyya,

Brihaspati Mondal

и другие.

The Science of The Total Environment, Год журнала: 2025, Номер 975, С. 179207 - 179207

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0
Dynamic shoreline alterations and their impacts on Olive Ridley Turtle (Lepidochelys olivacea) nesting sites in Gahirmatha Marine Wildlife Sanctuary, Odisha (India) DOI
Manoranjan Mishra, Saswati Pati, Suman Paul

и другие.

Marine Pollution Bulletin, Год журнала: 2024, Номер 202, С. 116321 - 116321

Опубликована: Апрель 3, 2024

Язык: Английский

Процитировано

3
Rapid impact assessment of severe cyclone storm Michaung along coastal zones of Andhra and Tamil Nadu, India: A geospatial analysis DOI
Manoranjan Mishra, Debdeep Bhattacharyya, Rajkumar Guria

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122369 - 122369

Опубликована: Сен. 10, 2024

Язык: Английский

Процитировано

2
Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India DOI
Rajkumar Guria, Manoranjan Mishra, Richarde Marques da Silva

и другие.

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 37, С. 101399 - 101399

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

2
Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid DOI Creative Commons
Devender Kumar Saini, Monika Yadav, Nitai Pal

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 24, 2024

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies strategies towards zero carbon emission require heavy influx renewable energy sources (RES) adoption electric systems. In addition, world has seen an increased frequency natural disasters. These events adversely impact grid, specifically less hardened distribution grid. Hence, a resilient network is demand future fulfill critical loads charging emergency vehicles (EV). Therefore, this paper proposes two-dimensional methodology planning operational phase Initially stochastic modelling EV load been performed duly considering geographical feature commute pattern form probability functions. Thenceforth, assessment earthquakes using damage state classification done model on efficacy proposed tested by simulating urban Indian with mapped DigSILENT PowerFactory integrated supervised learning tools Python. Subsequently 24-h profile before event after have compared analyze impact.

Язык: Английский

Процитировано

1
An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth DOI Creative Commons

Zhenxian Xu,

Xin Shen,

Sang Ge

и другие.

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Июль 18, 2024

Introduction As an exceptional geographical entity, the vegetation of Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in south-eastern sector QTP, serving as a transition area from sub-tropical evergreen broadleaf forest high-mountain vegetation. However, there has been limited exploration into predicting temporal and spatial variability cover using anti-interference methods address outliers long-term historical data. Additionally, correlation between these variables environmental factors natural forests with complex terrain rarely analyzed. Methods This study developed advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional cover) accurately evaluate predict time shifts FVC within BNNR, utilizing GEE (Google Earth Engine). satellite data utilized this paper consisted Landsat images spanning 1986 to2020. By integrating methodologies monitor assess trend, Hurst index was employed forecast FVC. Furthermore, association topographic evaluated, partial climatic influences analyzed at pixel level (30×30m). Results discussion Here are results research: (1) Overall, BNNR growth mean value increasing 59.40% 68.67% 2020. (2) TS-MK algorithm showed that percentage decreasing trend 59.03% (significant increase 28.04%) 22.13% decrease 6.42%), respectively. coupling exponent Theil-Sen estimator suggests majority regions projected sustain upward future. (3) Overlaying outcomes revealed changes were notably influenced by elevation. analysis indicated temperature exerts significant influence cover, demonstrating correlation.

Язык: Английский

Процитировано

0