气候变化领域集成服务门户(CCPortal): Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery

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DOI	10.1016/j.jag.2018.09.005
	Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery
	Hlatshwayo S.T.; Mutanga O.; Lottering R.T.; Kiala Z.; Ismail R.
发表日期	2019
ISSN	15698432
起始页码	65
结束页码	77
卷号	74
英文摘要	Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of R 2 = 0.70 and 0.25 for 2009–2011 and 2011–2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (R 2 = 0.88 and RMSE = 54.54 kg m −2 ) compared to two texture (R 2 = 0.85 and RMSE = 60.65 kg m −2 ) and single texture band combinations (R 2 = 0.64 and RMSE = 94.13 kg m −2 ). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB. © 2018
英文关键词	Biomass estimation; Image processing techniques; Reforestation; SPOT-6 imagery; Texture combinations
语种	英语
scopus关键词	aboveground biomass; estimation method; image processing; landfill; mapping; reforestation; satellite imagery; texture
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156570
作者单位	Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
推荐引用方式 GB/T 7714	Hlatshwayo S.T.,Mutanga O.,Lottering R.T.,et al. Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery[J],2019,74.
APA	Hlatshwayo S.T.,Mutanga O.,Lottering R.T.,Kiala Z.,&Ismail R..(2019).Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Hlatshwayo S.T.,et al."Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery".International Journal of Applied Earth Observation and Geoinformation 74(2019).