Best Management Practice Should Prioritize the Independent Farmers to Stop Haze

Abstract

The severe haze disaster in Southeast Asia requires quantification of the drivers of fire in Sumatra. Without a holistic method, the conclusions are inaccurate. This study used remote sensing data and Maxent modeling technique to model and predicted the distribution of fires in Riau, Sumatra. The MODIS hotspot data from 2001 to 2014 in the study area were gathered. The hotspot data were examined for the human-ignition factors such as deforestation, land management, land system, slope, and forest area status to understand the driver of fire. The results showed that the fire is human-caused. There were three main findings. First, the study area experienced rapid deforestation, with 1.7 million ha of forests was lost from 1990 to 2013. Second, the fire risk associated with unsustainable plantation development and unclear land tenure. The yearly hotspots were high soon after deforestation and reduced gradually. Most of the hotspot from 2001 to 2014 occurred in an area that developed for oil palm by the independent farmer (73.7%). In contrast, the area developed by the company (acacia, rubber, and oil palm) has fewer hotspots.Nevertheless, natural forests were shown to be fire-resistant. Third, the land system was the most important driver of fire, followed by landholders and deforestation.On the contrary, slope and forest area status showed the marginal driver of fire. These results indicated the importance of peat swamp forest, sustainable plantation management, and land tenure to mitigate haze disaster. Fire distribution modeling can develop fire risk maps that can help the government focus on high-risk areas.

Spatial Modeling of Forest and Land Fire Susceptibility Using the Information Value Method in Kotawaringin Barat Regency, Indonesia

“…Significant forest harvesting and conversion of abandoned land to palm oil plantations were the primary human causes of forest and land fires. These activities necessitate access to transportation networks, such as roads, rivers, or canal networks [34,48,50].…”

“…Some hotspot incidents occurred in remote areas, but these areas are easily accessible by river networks. Therefore, rivers and roads contributed to the ignition of forest and land fires [10,34]. This contradicts the required settlement distance.…”

“…Furthermore, the distances between rivers, roads, and settlements (Figure 3B-D) were obtained from their original data, which included line and point shapefiles. These data represent the accessibility of forested land to human activities [10,34]. data, which included line and point shapefiles.…”

“…data, which included line and point shapefiles. These data represent the accessibility of forested land to human activities [10,34].…”