Towards nationwide mapping of bamboo resources in the Philippines: testing the pixel-based and fractional cover approaches

Abstract

In tropical and subtropical countries, the awareness on the importance of bamboos to the environment and economy is increasing and so is the demand for spatial bamboo information. However, mapping bamboos especially those naturally grown has been challenging, as these grasses are often mixed with other land-use and land-cover (LULC). In this study, we used Sentinel 1 and Sentinel 2 remote sensing (RS) images, and their vegetation indices to accurately map the bamboos of Iloilo province in the Philippines using:(1) pixel-based method that mapped bamboos and other LULC at 10 m resolution, and (2) fractional cover method that mapped bamboo cover at 100 m resolution (% ha −1 ). A random forest model was trained for each method and then validated per hectare basis using a 50:50 training-validation ratio of a stratified random sample. The fractional cover method showed 0.34 higher Nash-Sutcliffe Efficiency (NSE) and 5.10% lower Root Mean Square Error (RMSE) than the pixel-based method. Further validation within upland and lowland sites also favoured the fractional cover method, but the results of the two methods were closer in the upland site (bamboo plantation). Errors at 10 m resolution especially in the lowlands were mostly commission errors, likely because of the spectral similarity and proximity between bamboos and > 14 vegetations. Averaging the RS inputs into 100 m resulted in at most 12% separation of reflectance values among bamboos, forests, and other vegetations. Using the bamboo cover map, a total of 14,795 (± 1,283) ha bamboos and 7.45 (± 4.20) million harvestable culms (poles) were estimated for the whole province, where 54% come from the lowland. We suggest using the fractional cover method for nationwide baselining of bamboo resources.

Exploring the Potential for Bamboo Agroforestry through a Value Chain Approach in Thailand, using Cloud-based Data Management on Google Earth Engine

“…Existing bamboo areas are often analyzed using RS imagery from satellites in combination with aerial photography taken from traditional aircrafts or drones, which have been developing since the late 1990s. These techniques can assess the global bamboo coverage and even distinguish the phenology of bamboo with the improvement of the technology over time (Dida et al, 2021). A relatively recent breakthrough of this remote imagery interpretation is enhanced by the advance of machine learning (ML) classi cation algorithms over the last ten years, which signi cantly increased the accuracy rate as shown in spatiotemporal dynamics of bamboo forests analysis from China (Qi et al 2018;You et al 2020).…”

Native bamboos with invasive traits: impacts, consequences and challenges with special emphasis on Asia Pacific Region

“…However, estimating the extent of bamboo-dominated forests is a challenging task as they grow dispersed and intermingled with other species in the forest understory with only some species reaching the canopy level (Lobovikov et al 2007;Wang et al, 2009). These may limit the use of GIS and RS in mapping the distribution bamboo invaders (Dida et al, 2021). The application of RS in bamboo forests is rather complicated not only due to their scattered distribution but also due to the difficulty in separating them from other co-occurring forest species.…”

“…Despite these challenges, there are some promising trials carried out in countries, such as China, India and Brazil, to quantify bamboo forests using RS (Bharadwaj et al, 2003;Linderman et al, 2004;Tang et al, 2016). Dida et al (2021) carried out an extensive study to quantify bamboo resources in the Philippines, but omitting those in the forest understory due to low visibility and limited inventory data for validation. FAO (2005) highlighted the lack of consistency in terms of the quality and the reliability of data on the distribution of bamboos among countries.…”

“…However, countries from the Asia-Pacific region have more accurate and comprehensive data on the distribution of bamboo forests compared to that of Latin America and Africa. Overall, the statistics related to bamboos are inconsistent due to the (i) lack of systematic inventories, methodologies and techniques, (ii) their position as an understory species, (iii) lack of records due to local trade, and (iv) incompatible definitions of 'bamboo forests' among countries (Dida et al, 2021). Therefore, more steps are needed to aptly identify and categorize bamboo forests, and more studies are to be conducted to fill some of these crucial knowledge gaps to face specific challenges.…”

“…In addition to revisiting the available literature to evaluate impacts, the knowledge gaps and potential challenges were identified and discussed. The need for more focus on the 'invasive' behavior of native species under different habitat-climate settings to make informed predictions of their further expansion has also been highlighted (Carey et al, 2012;Dida et al, 2021). Bamboos (Family: Poaceae) attract a global interest due to its uncommon life cycle, ecological significance, and a variety of commercial and cultural uses.…”