Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia
Sri Yulianto Joko Prasetyo1, Kristoko Dwi Hartomo1, Mila Chrismawati Paseleng1
1Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia.
Peerj. Computer Science
|June 4, 2021
Summary
This study developed a software framework using machine learning (ML) to predict aridity from LANDSAT 8 images. Random Forest (RF) and CART algorithms proved effective in identifying drought-prone areas.
Area of Science:
- Environmental Science
- Remote Sensing
- Data Science
Background:
- Aridity and drought monitoring are crucial for environmental management.
- Vegetation Indices (VI) derived from satellite imagery offer valuable insights into vegetation health and water stress.
Purpose of the Study:
- To develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images.
- To evaluate the performance of machine learning (ML) algorithms, specifically Random Forest (RF) and Correlation and Regression Trees (CART), for aridity prediction.
- To compare RF and CART with other ML models like Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn), and Multivariate Adaptive Regression Spline (MARS).
Main Methods:
- Image preprocessing of LANDSAT 8 OLI data.
- Calculation of various Vegetation Indices (VI) from satellite imagery.
- Application and comparison of ML algorithms (RF, CART, ANN, SVM, k-nn, MARS) for VI prediction.
- Accuracy assessment using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).
- Spatial interpolation of prediction results using Inverse Distance Weight (IDW).
Main Results:
- RF and CART algorithms demonstrated higher efficiency and effectiveness in predicting aridity compared to ANN, SVM, k-nn, and MARS.
- RF and CART achieved low prediction errors, with MAPE around 1.04%-1.05% and MSE as low as 0.05.
- Analysis of VI indicated widespread low vegetation areas, drought conditions, and low fire risk in the study region for 2020.
- The developed framework accurately predicts drought and land fire risks using VI data from LANDSAT 8 OLI imagery.
Conclusions:
- Machine learning algorithms, particularly RF and CART, provide accurate and stable predictions for drought and land fire risks.
- Vegetation Indices derived from LANDSAT 8 OLI imagery effectively capture vegetation condition and environmental factors relevant to aridity.
- The developed software framework offers a valuable tool for environmental monitoring and risk assessment.
Related Concept Videos
01:29Applications of GIS: Disaster Management and Emergency Response
240
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
240
01:17Precipitation and Co-precipitation
3.0K
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...
3.0K
02:41Responses to Drought and Flooding
11.3K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
11.3K
02:16Light Acquisition
8.8K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.8K
01:03Precipitation Gravimetry
9.5K
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...
9.5K
01:12Precipitation Processes
1.8K
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...
1.8K