Refining National Forest Cover Data Based on Fusion Optical Satellite Imageries in Indonesia

Precision mapping towards tropical forest cover data is critical to address the global climate crisis, such as land-based carbon measurement and potential conservation areas identification. In the recent decade, accessibility to open public datasets on forestry is rapidly increased. However, the availability of finer-resolution of forest cover data is still very limited. As a developing country with numerous rainforests, Indonesia suffered multifaceted threats, particularly deforestation. Thus, precise forest cover data can be useful to fulfill Indonesia’s nationally determined contribution to climate change. In this study, we mapped the national forest cover data for Indonesia using a new object-based image classification approach based on combined Planet-NICFI and Sentinel-2 optical imageries. Our findings had relatively high accuracy compared with the other studies, with the F score ranging from 0.67 to 0.99 and can capture the fragmented forest in fine resolution (i.e., ∼5 m). In addition, we found that Planet-NICFI bands had a higher contribution in predicting forest cover than Sentinel-2 imageries. Utilizing forest cover data for further analyses should be performed to help the achievement of national and global agenda, e.g., related to the FOLU net sink in 2030 and the Global Biodiversity Framework.

For the last few decades, ecosystem service has been the main issue in international nature conservation and rural development [1], and it is still a concern as the exploitation of natural resources, human-induced land use change, and global greenhouse gasses continue at a high rate [2]. Forests are not only affected by human activity but also serve an important role in mitigating upcoming threats such as landslides, floods, and loss of biodiversity [3]. Tropical rainforests, in particular, are known for their richness and contribution to the earth’s land-based ecosystem. Indonesia is one of the countries that have relatively massive forests, accounting for 39% of Southeast Asia’s forest extent [4]. Depending on the altitude and regional climate, it can range from lowland to mountainous forests. Each of these forest types contributes significantly to the ecosystem services that humans rely on, such as raw materials, reservoirs of biodiversity, soil protection, sources of timber, biomedicines, carbon sequestration, climate, and water regulations [5–7]. Indonesian tropical forests also play a critical role in the livelihood of local communities and the national economy [8].

However, deforestation has been one of the main issues of climate and biodiversity crises. The negative environmental consequences of tropical deforestation were far-reaching and long-lasting [9]. This rapid deforestation rate has contributed to biodiversity losses due to habitat degradation and fragmentation, particularly in Indonesia [10]. The latest studies from Margono et al. [11] suggest that forest loss in Indonesia has been recorded as one of the highest rates of primary losses in the tropics for the period 2002–2012, with annual primary forest cover loss in 2012 being the highest, totaling 0.84 Mha, more than the official forest loss report of Brazil (0.46 Mha). During the same period in other tropical rainforest countries, Mexico lost 0.28 Mha and Colombia with a primary forest cover loss of 0.69 Mha [8]. Indonesia, as a developing country, still struggles with infrastructure development which puts the forest with all the ecosystem services that it provides at risk [12]. Many policies drive investment in Indonesia to support economic growth in the form of infrastructure and land-based permits that will directly threaten forest cover. In Kalimantan and Sumatra, the amount of foreign investment toward infrastructure and extractive industries is five times greater than international funding for forest conservation schemes [13]. Barri et al. [14] analyzed that 50% of total deforestation (5.72 million hectares) in 2013–2017 occurred in logging concession, timber and oil palm plantations, and mining. Other numerous research studies also reported some factors that contribute to deforestation in Indonesia, such as road development [15], agricultural expansion [16], wildfires [17], and illegal logging and encroachment [18].

Halting deforestation and retaining the intactness of the forest ecosystem is a prevalent challenge in climate change mitigation [19], which may be assessed by the reliable assessment of carbon storage based on accurate mapping of forest types. Furthermore, the spatially explicit mapping of forest cover is critical for carbon stock estimation [20], wildfire behavior simulation [21], and wildlife habitat modeling [22]. In this regard, mapping the precise and reliable expected forest cover will support monitoring which can also be used as input in forest management and policymaking related to sustainable forest management.

With rising satellite availability and image resolutions, remote sensing data archives are continuously growing, possibly enabling users to access and analyze enormous time-series datasets. Remote sensing has become popular as a valuable tool for monitoring land cover, and it also works well for forest cover identification. Many previous research studies have shown that remote sensing data can predict forest and other land cover types with excellent accuracy [23–27]. In addition, combining two or more sensors can improve the model’s performance in depicting forest cover data [28–30].

The methods for identifying forest cover in Indonesia rapidly grew from 1995 until the recent years. Regarding [31], the map of Indonesia’s current land cover and land use was created using visual interpretation based on medium resolution imageries (i.e., Landsat). The accuracy of the forest cover classes is reported to be high (>90%), based on field verification and the operators’ local knowledge. However, visual-interpreting methods were relatively time-consuming, and the use of numerous interpreters over space and time compromises the consistency of the output map product [11]. Margono et al. [24] conducted a study about forest cover identification using a pixel-based method. Machine learning (ML) algorithms (e.g., random forest, support vector machine, and regression trees) typically produce better results than conventional classifiers since they do not require preconceptions regarding the distribution of the input data [32]. Machine learning is a subfield of artificial intelligence concerned with the development and investigation of systems that can learn from data. In the machine learning model, there are three approaches: supervised learning, semisupervised learning, and unsupervised learning. A machine learning system could, for instance, be trained on images to learn to differentiate between forest and nonforest images. After learning, it can then be used to classify new images into forest and nonforest object. The fandom forest algorithm is a classification method that used multiple and random subsets of data and features to produce multiple decision trees. A random forest classifier (RF) is a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest [32].

Currently, the need for forest cover data with very high spatial resolution is increasing to support monitoring, reporting, and decision-making [33]. Nevertheless, recently available data related to the precise forest cover data is very limited, e.g., global forest change (∼30 meters; [34]), PALSAR forest (25 meters; [35]), and Indonesia’s primary forest cover (30 meters; [24]). By mapping the presence of forests in Indonesia, a consistent forest distribution and area can be obtained, which can then be used as a base map and also as a reference for management and information in Indonesia. This is because the maps produced use inputs that are specific to conditions in Indonesia, so the maps that are the resulting data are more specific when compared to globally processed forest maps. Forest mapping is essential because it can be used to support preservation programs, such as efforts to protect and preserve biodiversity. In the presence of fragmented forests, the area and distribution of forests can sustain biodiversity existence. Fragmented and isolated forest sections vary greatly in ecology and composition and may not support the same level of biodiversity or ecosystem function as forests of the same size but within large forest systems [36]. Mapping of the forest in Indonesia also plays an essential role in forest management. The spatial and temporal variation in primary forest loss documents the continuing appropriation of natural forests within Indonesia, including the increasing loss of primary forests in wetlands and in land uses meant to limit or prohibit clearing, with implications for accurate greenhouse gas emissions estimation.

In this research, a random forest classifier (RF) is used to classify forest cover using an object-based image classification approach. The primary objective of this study is to demonstrate the simplicity of the random forest ensemble method and its efficacy in image classification. This study’s ultimate objective is to achieve the utmost classification accuracy by implementing high-quality image data acquired by a modern sensor (Sentinel-2 and Planet) and a mathematically robust classifier that is a random forest. Results from this study highlight the importance of spatially and temporally explicit data in bringing transparency to an important land use dynamic. Here we present a refined forest cover dataset at the national level in Indonesia with a spatial resolution of ∼5 meters based on spectral combinations from Sentinel-2A and Planet-NICFI imageries using the random forest algorithm. Moreover, we also evaluated our data using reference points to assess model performance and compared the forest cover data with another forest cover dataset. In addition, we also explored current forest cover dynamics in the 2017–2021 period to improve national forest monitoring in Indonesia.

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