Background: Mangoes are fruits of nutritional importance in Kenya. The fruits vary in their sugars depending on cultivar type and ripening stage. Current methods of sugar content analysis are based on HPLC methods, which are accurate but expensive and time-consuming. We evaluated the potential of diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy as a rapid tool for quantitative prediction of fructose and glucose sugars in the pulp of Kenyan mangoes.Methods: Principal component analysis and partial least regression models were developed using the first derivative DRIFT spectra (400–4000 cm1) to predict fructose and glucose sugars. A factorial analysis of variance compared effects of site (three locations), cultivar type (four cultivars), fruit position (inside/outside), and their interactions on fructose and glucose contents.Results: The principal component analysis scores plot using components 1 and 2 explained 75 and 8% of the variance, respectively, with no clear grouping either by sites or cultivars. The PLS range for glucose was R2 = 0.80, SECV = 0.55, and RPD = 11.52 and fructose R2 = 0.70, SECV = 0.28, and RPD = 11.52. Site (F (2, 265) = 18.12, p < 0.05, 2 = 0.02), cultivar type (F (3, 256) = 4.44, p < 0.05, 2 = 0.05), and fruit position (F (1, 259) = 7.62, p 0.05. In general, fruits outside the canopy had higher fructose and glucose contents than those within the canopy.Conclusions: DRIFTS coupled with chemometric
Tag: tree canopy
Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives
With the intensifying challenges of global environmental change, sustainability, and biodiversity conservation, the monitoring of the world’s remaining forests has become more important than ever. Today, Earth observation technologies, particularly remote sensing, are at the forefront of forest cover monitoring worldwide. Given the current conceptual understanding of what a forest is, canopy cover threshold values are used to map forest cover from remote sensing imagery and produce categorical data products such as forest/non-forest (F/NF) maps. However, multi-temporal categorical map products have important limitations because they inadequately represent the actual status of forest landscapes and the trajectories of forest cover changes as a result of the thresholding effect. Here, we examined the potential of using remotely sensed tree canopy cover (TCC) datasets, which are continuous data products, to complement F/NF maps for forest cover monitoring. We developed a conceptual analytical framework for forest cover monitoring using both types of data products and applied it to the forests of Southeast Asia. We conclude that TCC datasets and the statistics derived from them can be used to complement the information provided by categorical F/NF maps. TCC-based indicators (i.e. losses, gains, and net changes) can help in monitoring not only deforestation but also forest degradation and forest cover enhancement, all of which are highly relevant to the 2030 Agenda for Sustainable Development and other global forest cover monitoring-related initiatives. We recommend that future research should focus on the production, application, and evaluation of TCC datasets to advance the current understanding of how accurately these products can capture changes in forest landscapes across space and time.