Characterization and Estimation of Dates Palm Trees in an Urban Area Using GIS-Based Least-Squares Model and Minimum Noise Fraction Images

Abstract

Date palm is the major food source and possesses an important role in the economic aspects, environmental parts, and society. These crops were subjected to degradation due to the financial and numerous military conflicts. Because of the expensive cost of monitoring and managing date palm in field measurements, and limited studies using satellite images, the authors proposed a method to estimate and map date palm using the Landsat-8 satellite images. The authors applied the least-squares multiple regression and GIS techniques to find suitable predictors from the set of variables such as original bands of Landsat-8, Minimum Noise Fraction (MNF) transformation, tasseled cap component transformation, and spectral index. In order to validate the proposed method, the field measurement data were utilized to assess the estimated date palm from the Landsat-8 images. A linear combination of MNF Landsat-8 band 4 (red, 0.636-0.673 µm), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI) were the best date palm predictor (R2adj= 0.988, root-mean-squared error (RMSE) = 0.013). The results demonstrate that the MNF Landsat-8 images in the least square regression help improve the date palm estimation and mapping for the practical use in the study area with high accuracy.

Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques

“…This is due to performances of machine learning techniques differing based on genetic variability. The vegetation indices related to water in the plant [36] and the data, corresponding to the average and the 25th and 75th percentiles, are the variables that have the greatest influence on the performance of the proposed prediction models. In the best models (NN and RF), six of the fifteen most important variables correspond to percentile vegetation indices (25th and 75th quartiles).…”

Utilization of Geographic Information System for hydrological analyses: A case study of Karbala province, Iraq

“…Figure1-The map of the study area[16].2.2 Data collectionThe data were collected from the Ministry of water resources, Iraq, observed in the field; the total number of 350 points DGPS with technique static were collected using a DGPS device. The device accuracy for horizontal is (±≤5 cm), and vertical is (±≤10cm)[16,17]. Table(1) shows some of the data for the study area.…”