Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Assessment of the Efficiency of Climatic factors and geomorphometry in predicting vegetation percentages based on machine learning processes

Introduction: Rangelands are natural ecosystems having large genetic resources. Since plant vegetation is the bed of life on earth and changes under the influence of surrounding environmental elements, using environmental element can highly contribute to estimate vegetation percent more accurately. Two effective elements which can contribute to estimate the vegetation distribution are climatic ...

Georeferencing Semi-Structured Place-Based Web Resources Using Machine Learning

In recent years, the shared content on the web has had significant growth. A great part of these information are publicly available in the form of semi-strunctured data. Moreover, a significant amount of these information are related to place. Such types of information refer to a location on the earth, however, they do not contain any explicit coordinates. In this research, we tried to georefer...

Computing the Rate of Disappearance of Cropland Using Satellite Images

Evaluating machine learning methods and satellite images to estimate combined climatic indices

The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role ...