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Machine learning could help save mangrove forests

Mangrove forests, essential components of tropical and subtropical coastal zones, provide numerous goods and ecosystem services crucial for ecological balance. However, these critical habitats are under threat as they continue to disappear and degrade across the globe. 

One way to facilitate effective conservation and promote policies for mangrove protection is to carefully assess these habitats, monitor their changes, and identify fragmented areas. Unfortunately, obtaining this vital information is often a challenging endeavor.

Dr. Neda Bihamta Toosi, a postdoc at Isfahan University of Technology in Iran specializes in landscape pattern changes using remote sensing. She explained that since mangrove forests are located in tidal zones and marshy areas, they are hardly accessible.

Mangrove ecosystems of Qeshm Island in the Persian Gulf, Iran
Image Credit: Neda Bihamta Toosi

In a recent study published in the journal Nature Conservation, Dr. Bihamta Toosi, alongside a team of authors, investigated the potential of using machine learning to classify these delicate ecosystems.

How the study was done

To compare the performance of various combinations of satellite images and classification techniques, the researchers assessed how well each method could map mangrove ecosystems. 

“We developed a novel method with a focus on landscape ecology for mapping the spatial disturbance of mangrove ecosystems,” said Dr. Bihamta Toosi. “The provided disturbance maps facilitate future management and planning activities for mangrove ecosystems in an efficient way, thus supporting the sustainable conservation of these coastal areas.”

The study’s findings revealed that object-oriented classification of fused Sentinel images significantly improved the accuracy of mangrove land use/land cover classification. 

“Assessing and monitoring the condition of such ecosystems using model-based landscape metrics and principal component analysis techniques is a time- and cost-effective approach,” said Dr. Bihamta Toosi. “The use of multispectral remote sensing data to generate a detailed land cover map was essential, and freely available Sentinel-2 data will guarantee its continuity in the future.”

By utilizing this innovative approach, the research team believes that they can supply information on land cover change trends that influence the development and management of mangrove ecosystems, thereby promoting improved planning and decision-making. 

“Our results on the mapping of mangrove ecosystems can contribute to the improvement of management and conservation strategies for these ecosystems impacted by human activities,” wrote the researchers. This groundbreaking work presents a promising avenue for safeguarding these vital ecosystems and ensuring their preservation for generations to come.

More about mangrove forests

Mangrove forests are unique ecosystems that thrive in the coastal intertidal zones of tropical and subtropical regions. They consist of various species of mangrove trees, which have developed unique adaptations to survive in these harsh, saline, and oxygen-poor environments. Some key facts and features about mangrove forests include:


Mangrove trees have evolved special adaptations to survive in their challenging environment. These include aerial roots, which help them absorb oxygen from the air, and salt filtration mechanisms that allow them to thrive in saline water.


Mangrove forests support a diverse array of flora and fauna. They provide essential habitat for numerous species, including fish, crustaceans, mollusks, birds, and reptiles. Many of these species are commercially important or endangered.

Coastal protection

Mangrove forests act as natural barriers against coastal erosion, storm surges, and tsunamis. Their dense root systems help stabilize sediments and protect shorelines from erosion caused by waves and currents.

Carbon sequestration

Mangroves are highly efficient at capturing and storing carbon dioxide from the atmosphere, making them a critical component in mitigating climate change. They can sequester carbon at rates 3-5 times greater than terrestrial forests.

Nursery grounds

Mangrove forests serve as nursery grounds for numerous fish and crustacean species, many of which are vital for local and global fisheries. These habitats offer protection from predators and support the growth of juvenile organisms.


Mangrove forests are crucial for the livelihoods of millions of people living in coastal areas, providing resources like timber, fuelwood, and non-timber forest products. They also support activities such as fishing, aquaculture, and ecotourism.

Ecosystem services

In addition to the benefits mentioned above, mangrove forests also offer other ecosystem services, such as water filtration, nutrient cycling, and sediment retention, which contribute to overall coastal ecosystem health.

Despite their importance, mangrove forests face multiple threats, including deforestation, pollution, climate change, and coastal development. To ensure the preservation of these valuable ecosystems, it is essential to invest in conservation efforts, sustainable management practices, and public awareness campaigns.

More about machine learning

Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data without explicit programming. 

Machine learning has advanced rapidly in recent years, transforming various industries and aspects of daily life. Here are some key concepts and applications related to machine learning:

Supervised Learning

Supervised learning is the most common type of machine learning, in which the algorithm is trained on labeled data. The algorithm learns from input-output pairs, and its goal is to make accurate predictions for new, unseen data based on the learned patterns.

Unsupervised Learning

Unsupervised learning deals with data that has no labels or output values. The primary goal is to identify patterns, structures, or relationships within the data, such as clustering or dimensionality reduction.

Reinforcement Learning

In reinforcement learning, an agent learns to make decisions by interacting with an environment to achieve a specific goal. The agent receives feedback in the form of rewards or penalties and adjusts its behavior accordingly to maximize the cumulative reward.

Deep Learning

Deep learning is a subset of machine learning that employs artificial neural networks with multiple layers to model complex patterns in data. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are popular types of deep learning models, often used for image and language processing tasks, respectively.

Natural Language Processing (NLP)

NLP is a field that combines machine learning, linguistics, and computer science to enable computers to understand and process human language. NLP techniques are used for tasks such as sentiment analysis, machine translation, and chatbot development.

Computer Vision

Computer vision is the field of study that enables computers to interpret and analyze visual information from the world. Machine learning plays a crucial role in tasks such as image recognition, object detection, and facial recognition.

Recommendation Systems

Machine learning is widely used in recommendation systems, which provide personalized suggestions to users based on their preferences and behaviors. These systems are common in applications such as e-commerce, online advertising, and content platforms like Netflix and Spotify.

Anomaly Detection

Machine learning algorithms can be trained to identify unusual patterns or deviations from the norm, which is useful for detecting fraud, network intrusions, or equipment failures.

As machine learning continues to evolve, its applications are expected to expand across various industries, including healthcare, finance, transportation, and agriculture. The rapid growth of machine learning has also raised concerns regarding ethical considerations, such as privacy, security, and fairness, which must be addressed to ensure the responsible development and deployment of AI technologies.


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