Planetscope Nanosatellites Image Classification Using Machine Learning

Mohd Anul Haq

doi:10.32604/csse.2022.023221

Abstract

To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RF-based classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.

Highlights

Agriculture is a crucial sector in Saudi Arabia due to the increasing population and significant economic growth [1]
Our findings indicated that the agricultural activities were almost similar for 60 pivot agriculture fields analyzed in the present investigation
The z-value is below −1.96, and the p-value is < 0.05 for all three classes; it can be concluded that there is a strong correlation between Normalized Difference Vegetation Index (NDVI) and Random Forest (RF), so the null hypothesis is rejected

Summary

Introduction

Agriculture is a crucial sector in Saudi Arabia due to the increasing population and significant economic growth [1]. Despite various limitations, such as changing climate, less rainfall, limited water resources, hyper aridity, and scattered cultivatable areas, agriculture has prioritized improving food security and achieving self-dependency [2]. Agricultural activities mainly depend on the availability of water consumed from aquifers (shallow/deep) and seasonal water in an arid area [3]. It makes it very crucial to estimate the water consumption for planning the water resources. Due to the unavailability of water use at the microlevel, satellite-based GRACE data becomes a vital utility to estimate water extraction.

Methods

Results

Conclusion