Related Experiment Video
Updated: Oct 13, 2025
07:13
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
4.0K
Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand
Mohd Anul Haq1, Prashant Baral2, Shivaprakash Yaragal3
1Department of Computer Science, College of Computer Science and Information Sciences, AL-Majmaah 11952, Saudi Arabia.
Sensors (Basel, Switzerland)
|November 13, 2021
Summary
This study analyzed 17 years of climate data in Uttarakhand, India. It found increasing snow cover and decreasing vegetation, highlighting the need for regional climate change analysis.
Area of Science:
- Environmental Science
- Climate Science
- Remote Sensing
Background:
- Regional climate change studies in the Himalayas often lack long-term, large-scale data.
- Understanding climate parameter trends is crucial for assessing climate change impacts in the region.
Purpose of the Study:
- To provide a comprehensive overview of vegetation, snow cover, and temperature changes in Uttarakhand, India.
- To assess the potential of machine learning for predicting environmental variables using remotely sensed and climate data.
Main Methods:
- Bulk processing of Moderate Resolution Imaging Spectroradiometer (MODIS) data.
- Analysis of meteorological records and simulated global climate data.
- Application of Support Vector Machines and Long Short-term Memory (LSTM) networks for predictive modeling.
Main Results:
- Observed increasing trends in snow-covered areas during the pre-monsoon season since 2001.
- Detected decreasing trends in vegetation cover during the monsoon season.
- Validated MODIS-derived land surface temperature (LST) against global climate data, showing close agreement.
Conclusions:
- Remotely sensed and simulated climate data are reliable for future climate trend studies in Uttarakhand.
- Solar radiation and cloud cover significantly influence lapse rate variations.
- The study underscores the utility of integrated data sources and machine learning for regional climate change assessment.
More Related Videos
12:26Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
Published on: October 11, 2016
13.5K
09:44Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
Published on: October 16, 2018
10.4K
Related Concept Videos
01:12Precipitation Processes
698
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
698
01:50Global Climate Change
25.0K
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
25.0K
01:21Manipulation and Analysis
90
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
90
01:03Precipitation Gravimetry
8.1K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
8.1K
01:21Isochoric and Isobaric Processes
3.8K
A thermodynamic process that occurs at constant volume is called an isochoric process. According to the first law of thermodynamics, heat supplied or removed from the system is partially utilized to perform work and change the internal energy of the system. However, in an isochoric process, the volume remains constant. Hence, the work done by the system is zero. Therefore, the exchange of heat changes the internal energy of the system only.
Suppose 1000 g of water is heated from 40...
Suppose 1000 g of water is heated from 40...
3.8K
01:17Precipitation and Co-precipitation
2.7K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
2.7K