Project title: Regional Forest Observations for Sustainable Forest Management
Executing agency: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry
Implementing agency: General Directorate of Administration for Nature Conservation and Protection/ MOE, Cambodia; Guangxi Forest Inventory & Planning Institute, China; Guangxi University, China; Faculty of Forestry of National University of Laos; Forest Research Institute Malaysia; Forest Department, Myanmar; Royal Forest Department, Thailand; Forest Inventory & Planning Institute, Viet Nam; Southwest Forestry University, China
Budget in USD (total / APFNet grant): 699,860/499,860
Target economies: Lao PDR, Cambodia, Myanmar, Thailand, Vietnam, Malaysia, China
Objectives: To further enhance the capacity on regional level forest resource monitoring and analysis through applying medium resolution remote sensing data, analyze forest changes, and link the change characteristics with forest polices; To enhance the capacity on stand level forest inventory through applying high resolution remote sensing data and airborne laser scanning technology; To further strengthen the network on forest monitoring in the region through establishing a mechanism for regional forest observations and provide related capacity building supports.
Expected outputs: Forest coverage map of 2017 at 30 m spatial resolution; Forest change and driving forces analysis during 2005~2017; Stand level inventory maps using high resolution data in selected sites; Estimated forest carbon maps in selected sites using airborne laser scanning technology; Establish a mechanism for regional forest observations; Enhanced through the Regional Forest Observations (RFO) mechanism in the region.
The Greater Mekong Subregion (GMS), including Cambodia, China (Yunnan and Guangxi province), Lao PDR, Myanmar, Thailand, and Viet Nam as well as Malaysia in Southeast Asia are rich in forest resources and biodiversity. However, along with the rapid development in the GMS region, forests face an increasing number of threats and pressures. How to effectively evaluate and assess the forest resources on a large scale has become a common concern of many economies especially the developing economies in GMS region. The previous APFNet-funded project “Forest Cover and Carbon Mapping in the Greater Mekong Subregion (GMS) and Malaysia” has developed algorithms for forest cover mapping and carbon estimation, produced forest maps of 2005 and 2010, and a forest aboveground biomass map of 2005, which have provided significant baseline analysis and assessment on the forests resource change in the region and enhanced the capacity of economies in the GMS region in forest resources monitoring. This new project is the second phase of that project, aiming to further enhance broader forest resources monitoring on the regional scale in the GMS and Malaysia to identify driving forces for forest change. On selected sites the latest high resolution technology like LiDAR will be used to obtain a multitude of information that traditionally can only be obtained through on-the-ground monitoring.
Seeing how and why forests change in the region
Many projects or research have worked on forest resource monitoring using multi-temporal earth observation data to characterize e.g. annual global forest change or global land cover maps. Such multi-temporal remote sensing products are extremely useful tools to identify driving forces for forest change as well. They provide a key source of information for the crackdown on illegal logging, forest fire monitoring and early warning and reduction of forest degradation, forest gain from afforestation and reforestation, and the improvement of forest quality. Also, forest monitoring to support sustainable forest resources management can provide the earth observation data and technical support needed by economies to fulfil their obligations effectively arising from international environmental agreements. Based on the Phase I project achievement, this project will further enhance forest monitoring in the GMS region through applying advanced remote sensing technology on a regional scale and strengthen the network on regional forest monitoring by establishing a mechanism for regional forest observations. The approach will integrate multiple sources remote sensing data, ground measurements and geospatial technologies. Throughout the project, a forest cover map for the GMS region for 2017 at medium resolution will be produced. Then, together with the forest cover maps of 2005 and 2010 produced in Phase I, the driving forces for forest change will be analyzed, while taking forest policies and international factors into consideration. The outcomes will help to clarify how, when, where and why the forests changes in each economy. For selected sites, stand-level inventory method using high-resolution remote sensing data and airborne laser scanning technology will be developed and demonstrated.
Remote but detailed: Stand Level Forest Inventory through high resolution remote sensing and airborne laser scanning
While on a regional scale the analysis of forest cover change and its drivers can reveal new, unexpected insights, the data is only of limited use for the local forest manager as they will need a lot more information about any given stand, such as tree height, canopy density, forest volume or forest carbon. For this reason on selected sites high resolution remote sense data and LiDAR (light detection and ranging) technology will be employed to obtain such data.
Lidar uses ultraviolet, visible, or near infrared light to image objects. It can target a wide range of materials, including non-metallic objects, rocks, trees, rain, chemical compounds, aerosols, clouds and even single molecules. A narrow laser beam can map physical features with very high resolutions; for example, an aircraft can map terrain at 30-centimetre resolution or better. In applying this technology, large amounts of time can be saved while also necessitating less staff to be on-site for on-the-ground monitoring. As forest departments in the GMS region and Malaysia are often locally understaffed, this approach promises to lighten the workload while providing the kind of high-quality data needed for evidence-based sustainable forest management. As the technology is fairly new, local staff will be trained in using both high-resolution remote sensing data and LiDAR for stand assessments in the future.