Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

Mohamed Abouleish,Mohamed M Mohamed,Samy I Elmahdy,Fares M Howari,Tarig A Ali,Daniel Simonet

doi:10.3389/fenvs.2020.00102

Mohamed Abouleish, Mohamed M Mohamed + Show 4 more

Open Access

https://doi.org/10.3389/fenvs.2020.00102

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Abstract

Mangrove forests are acting as a green lung for the coastal cities of the United Arab Emirates, providing a habitat for wildlife, storing blue carbon in sediment and protecting shoreline. Thus, the first step toward conservation and a better understanding of the ecological setting of mangroves is mapping and monitoring mangrove extent over multiple spatial scales. This study aims to develop a novel low-cost remote sensing approach for spatiotemporal mapping and monitoring mangrove forest extent in the northern part of the United Arab Emirates (NUAE). The approach was developed based on random forest (RF), Kernel logistic regression (KLR), and Naive Bayes Tree (NBT) machine learning algorithms which use multitemporal Landsat images. Our results of accuracy metrics include accuracy, precision, recall, F1 score revealed that RF outperformed the KLR and NB with an F1 score of more than 0.90. Each pair of produced mangrove maps (1990-2000, 2000-2010, 2010-2019 and 1990-2019) was used to image difference algorithm (ID) to monitor mangrove extent by applying a threshold ranges from +1 to -1. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization.

Highlights

Mangroves are woody plants that are extensively distributed in intertidal and estuary zones and their forests cover thousands of hectares along the shorelines (Sherrod and McMillan, 1985; Field et al, 1998)
Mangroves provide a wide range of benefits to the economy and the environment as they play a vital role in ecology
random forest (RF) yielded high precision, high recall and F1 score. This means that the RF has a powerful ability to map the mangrove forests in different ecosystems

Summary

INTRODUCTION

Mangroves are woody plants that are extensively distributed in intertidal and estuary zones and their forests cover thousands of hectares along the shorelines (Sherrod and McMillan, 1985; Field et al, 1998). Integration of RF, Kernel logistic regression (KLR), Naive Bayes Tree (NBT), and Image difference (ID) have shown the ability to achieve classification over a regional scale and precise monitoring extent of the mangrove (Colkesen and Kavzoglu, 2017) These techniques can reduce the variance and overfitting of the classification maps and assess many variables separately compared to traditional classifiers, such as maximum likelihood (Ha et al, 2020). Random Forest To classify and map mangrove forests in an accurate and low-cost way, it is important to employ machine learning algorithms, learn these algorithms with training datasets with a higher spatial resolution as well as algorithm optimal parameterization (Huang et al, 2009; Elmahdy and Mohamed, 2018). Monitoring the NUAE mangrove changes were performed using a change detection tool implemented in the Envi v.4.5 software

RESULTS

DISCUSSION

DATA AVAILABILITY STATEMENT

CONCLUSION