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Map of Iloilo province showing administrative boundaries and neighbouring provinces. The boundary data comes from global administrative areas (GADM) database.
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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,...
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Context 1
... study area is Iloilo province, the largest province (5,001 km 2 ) in the Western Visayas region, Philippines. The province is situated between 10°20ʹ to 11°40ʹ N and 121°50ʹ to 123°25ʹ E (Figure 1). The climate within the province falls under the Modified Corona Type I (dry and wet seasons) and Type III or less pronounced dry and wet seasons (Kintanar 1984). ...
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... addition, two bamboo landscapes were validated to assess how the mapping methods differ between the uplands and lowlands (recall Figure 1). For the upland site, we selected a portion of a bamboo plantation in the municipality of Maasin; while for the lowland site, we pre-identified a community with bamboos in backyards and roadsides. ...
Context 3
... this map, the total area of bamboos in Iloilo was estimated at 14,795 (± 1,283) ha. In Figure 6 (a), it can be observed that bamboos were spread within the whole province and the densest cover were found in the mid-latitudes of the province (corresponding to the location of the municipalities of Maasin, Alimodian, and Janiuay, see Figure 1). Moreover, patches of dense bamboos were found in the southwest bound of the province, while lesser bamboos were observed going northeast. ...
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... 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 That process worked on numerous geospatial datasets collected from public access or state authorities to evaluate several earth surface conditions leading to the evidence-based prediction of feasibility for new potential bamboo agroforestry sites. ...
The global construction sector consumes almost half of the world's total material production footprint, implying it is the highest single-category material footprint across the global economy. In the search for nature-based alternatives, bamboo grass can become a mainstream building material in the global tropical belt due to significantly shorter rotation times compared to softwood or hardwood species.
To determine bamboo potential within a value-chain work frame, the first step is to evaluate the potential land for cultivation. Technically driven soil selection, with optimal climatic conditions, can generate culms taller than 20 m or, on the contrary, culms of a maximum of 6 to 7 m in height. The correct choice of soil also favors the plantation owner since faster plant development will be observed; the plantation will show a higher number of culms, larger diameters, cheaper production, and shorter times until plant maturity, thus requiring less time to recover the initial investment.
This research presents a remote-sensing-based tool for surveying and exploring bamboo agroforestry potential over the entire national territory of Thailand based on climatic conditions, altitude, topography, existing land cover, and soil characteristics (texture and acidity).
Through the implementation of the research, a total amount of 345,838 km² with viable growth conditions was discovered. 45,968 km² (13.29%) show basic compatibility levels, 242,198 km² (70.03%) show intermediate levels and 57,672 km² (16.68%) present optimal growth conditions. The corresponding regions were located on an interactive geoportal with a 100 m-per-pixel resolution and the ability to benchmark the individual selection criteria.
... 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 Based mostly on GIS, a similar technique was applied as early as 2010 for the potential bamboo research in Mozambique, which uses topography, physiography, climate, and infrastructure layers to create output maps such as the optimal location for bamboo-based charcoal plant set-up, or suitable areas for bamboo nurseries establishment (International Union for Conservation of Nature [IUCN] 2010). ...
The global construction sector consumes almost half of the world's total material production footprint, implying it is the highest single-category material footprint across the global economy. In the search for nature-based alternatives, bamboo grass can become a mainstream building material in the global tropical belt due to significantly shorter rotation times compared to softwood or hardwood species.
To determine bamboo potential within a value-chain work frame, the first step is to evaluate the potential land for cultivation. Technically driven soil selection, with optimal climatic conditions, can generate culms taller than 20 m or, on the contrary, culms of a maximum of 6 to 7 m in height. The correct choice of soil also favors the plantation owner since faster plant development will be observed; the plantation will show a higher number of culms, larger diameters, cheaper production, and shorter times until plant maturity, thus requiring less time to recover the initial investment.
This research presents a remote-sensing-based tool for surveying and exploring bamboo agroforestry potential over the entire national territory of Thailand based on climatic conditions, altitude, topography, existing land cover, and soil characteristics (texture and acidity).
Through the implementation of the research, a total amount of 345,838 km² with viable growth conditions was discovered. 45,968 km² (13.29%) show basic compatibility levels, 242,198 km² (70.03%) show intermediate levels and 57,672 km² (16.68%) present optimal growth conditions. The corresponding regions were located on an interactive geoportal with a 100 m-per-pixel resolution and the ability to benchmark the individual selection criteria.
... Liu et al. (2016) used the bidimensional empirical mode decomposition (BEMD) algorithm to fuse data from GF-1 image multispectral band, panchromatic band and TerraSAR-X band, and the results showed that the BEMD method can effectively enhance the accuracy of bamboo forest classification, and the fused data also have better interpretation accuracy. Dida et al. (2021) used Sentinel 1 and Sentinel 2 data and their vegetation indices to map bamboo coverage in Iloilo Province, Philippines. As a result, it is necessary to combine multisource remote sensing data for bamboo forest information extraction at the regional or global scale, which has significant advantages. ...
Bamboo groves predominantly thrive in tropical or subtropical regions. Assessing the efficacy of remote sensing data of various types in extracting bamboo forest information from bright and shadow areas is a critical issue for achieving precise identification of bamboo forests in complex terrain. In this study, 34 features were obtained from Sentinel-1 SAR and Sentinel-2 optical images using the Google Earth Engine platform. The normalized shaded vegetation index (NSVI) was then employed to segment the bright and shadow woodlands. Different features from diverse data sources were evaluated to extract bamboo forest information in the bright and shadow areas, then use the random forest (RF) classification algorithm to extract bamboo forest. The results showed that (1) the red-edge and short-wave infrared bands of Sentinel-2 optical images and their corresponding vegetation indices are significant in bamboo forest information extraction. (2) The dissimilarity and homogeneity of Sentinel-2 texture features in the bright area and dissimilarity in the shadow area, the Sentinel-1 backscatter features in the bright area and the VV and VH in the bright area and VV-VH in the shadow area have some variability between bamboo and nonbamboo forests, which can be used as effective features for bamboo forest extraction. (3) The combination of spectral, texture and backscatter features yields the highest overall classification accuracy and Kappa coefficient, at 87.96% and 0.7435, respectively. This study has the potential for remote sensing refinement of bamboo forest identification in complex terrain areas by utilizing subregion classification methods combined with optical and radar image features.
... Additionally, Zhao et al. (2018) developed a contemporary bamboo map of Ethiopia, Kenya, and Uganda by using an RF classifier. Dida et al. (2021) mapped the bamboos of Iloilo province in the Philippines by using an RF model. The variety of constantly updated geospatial datasets and inbuilt algorithms in GEE also provided a powerful platform for mapping the spatial distribution of bamboo with remarkable computation speed. ...
Bamboo is considered one of the world’s highest-yielding renewable natural resources. International Network of Bamboo and Rattan has identified that the effective utilization of bamboo resources can help in realizing at least six United Nations sustainable development goals. Therefore, the information about the spatial distribution and area under bamboo is essential for its better management and conservation. Hence, this paper systematically reviewed and compiled the published literature around the globe which rigorously focussed on mapping bamboo resources worldwide using remote sensing. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was adopted and a total of 46 papers published between 1991 and 2021 were evaluated based on the relevant criteria. It was observed that most of the studies on bamboo mapping were carried out using medium-resolution freely available satellite images. Around 47% of the studies utilized Landsat MSS, TM, ETM+ & OLI data. The classification methods widely used for mapping bamboo were found to be the visual interpretation and maximum likelihood classifiers. However, after 2014 the studies emphasized more on using machine learning algorithms for accurate mapping of bamboo. In addition to that, the use of the Google Earth Engine cloud computing platform showed great potential for bamboo mapping by accessing a plethora of freely available datasets and classification algorithms. Spectral bands and vegetation indices were the most common variables used for bamboo mapping. The global overview highlighted that very little research on bamboo mapping has been carried out in bamboo-rich countries, except in China. This compilation will help in understanding the gaps related to the mapping and monitoring of this important natural resource worldwide.
... 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). ...
... 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. ...
Defying all norms of biological invasions, some native species expand their populations similar to their exotic counterparts causing potentially harmful impacts in native habitats. Despite an early caution by ecologists, they are now recognized as ‘native invaders’. Though ‘native’ invaders may also incur harmful impacts similar to their ‘exotic’ counterparts, there are clear contrasts between them, thus demanding further studies to explore their life traits and cues that trigger their invasive traits. Among native invaders, bamboos are in the forefront due to their robust growth and resilience to harsh conditions. Also, it is a known fact that bamboo-dominated forests are on the increase globally while native forest are declining at a rapid rate. This review attempts to condense the current understanding of ‘native’ bamboos that spread in the Asia Pacific region with invasive potential and their short- and long-term ecological impacts. Possible environmental cues that may trigger their ‘invasive’ nature are also discussed. Of many, climate change seems to be the major driving force triggering their invasive behavior, though long-term studies are needed to ratify this link. Major challenges and knowledge gaps that hamper their control have also been deliberated. The evidence confirmed that native bamboos have the potential to incur negative impacts on ecology, social and economic aspects. However, their impacts are not always in parallel with that of ‘exotic’ invaders, thus cautioning any attempt of generalization. The lack of comprehensive research and historical information are considered as major impediments to identify suitable measures to manage them effectively. Further studies are mandatory to fill the existing knowledge gaps and to identify challenges to bring about effective management strategies to control ‘native’ bamboos with invasive potential.
The present study was conducted to understand the key ecological and biological questions of conservation importance in Drepanostachyum falcatum which aimed to map potential distribution in the western Himalayas and decipher spatial genetic structure. Eco-distribution maps were generated through ecological niche modelling using the Maximum Entropy (MaxEnt) algorithm implemented with 228 geocoordinates of species presence and 12 bioclimatic variables. Concomitantly, 26 natural populations in the western Himalayas were genetically analysed using ten genomic sequence-tagged microsatellite (STMS) markers. Model-derived distribution was adequately supported with appropriate statistical measures, such as area under the 'receiver operating characteristics (ROC)' curve (AUC; 0.917 ± 0.034)", Kappa (K; 0.418), normalized mutual information (NMI; 0.673) and true skill statistic (TSS; 0.715). Further, Jackknife test and response curves showed that the precipitation (pre- and post-monsoon) and temperature (average throughout the year and pre-monsoon) maximize the probabilistic distribution of D. falcatum. We recorded a wide and abundant (4096.86 km2) distribution of D. falcatum in the western Himalayas with maximum occurrence at 1500 to 2500 m asl. Furthermore, marker analysis exemplified high gene diversity with low genetic differentiation in D. falcatum. Relatively, the populations of Uttarakhand are more genetically diverse than Himachal Pradesh, whereas within the Uttarakhand, the Garhwal region captured a higher allelic diversity than Kumaon. Clustering and structure analysis indicated two major gene pools, where genetic admixing appeared to be controlled by long-distance gene flow, horizontal geographical distance, aspect, and precipitation. Both the species distribution map and population genetic structure derived herein may serve as valuable resources for conservation and management of Himalayan hill bamboos.
