Abstract
In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT), support vector machine (SVM), and random forest (RF)—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.
Highlights
Mangroves are salt-tolerant evergreen woody plants, distributed in the inter-tidal region in tropical and subtropical regions [1]
Through a visual comparison with the field survey data and the original 0.5-m image, we found that the north was almost covered with Sonneratia apetala group 1 (SA1) and Sonneratia apetala group 2 (SA2), which indicated that Sonneratia apetala was apparently under-represented in pixel-based classification, especially in decision tree (DT)
Compared with the field surveying data and original images, we found that half of these islands in the center were covered with Hibiscus tiliaceus (HT) and the other half were occasionally presented mangroves species (OT), which indicated that the random forest (RF) based classification was more consistent with real mangrove species
Summary
Mangroves are salt-tolerant evergreen woody plants, distributed in the inter-tidal region in tropical and subtropical regions [1]. They provide breeding and nursing grounds for marine and pelagic species and play an important role in windbreak, shoreline stabilization, purification of coastal water quality, carbon sequestration, and maintenance of ecological balance and biodiversity [2]. During the past 50–60 years, mangrove forests in China have been largely reduced from 420 km; in the 1950s to 220 km in 2000 due to agricultural land reclamation, urban development, industrialization, and aquaculture [3]. In the past 20 years, with the increasing efforts in environmental protection from the Chinese government, artificial mangroves have been widely planted in China [4,5]. Artificial mangroves have the characteristics of monoculture and low survival rates
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