Browsing by Subject "vegetation indices"
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(2020)Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.
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(2024)Grass biomass has many important and diverse roles for ecosystems functioning, the carbon cycle, rangeland productivity and local livelihoods. Quantifying and understanding grass biomass in dynamic savanna ecosystems during dry season is important for sustainable land management and monitoring grazing pressures, especially amidst climate change. Traditional ground-based methods to assess vegetation are subjective and time consuming, while remote sensing provides efficiency in monitoring grass biomass at large scales. Grass biomass assessments using remote sensing data have been extensively conducted worldwide, but such research in African savannas remains rare. This study aimed to study connections between dry season grass biomass measured in savanna rangelands and airborne hyperspectral imagery data obtained simultaneously in LUMO Conservancy area of South-Eastern Kenya. Two modelling techniques were compared: averaged plot values (n=24) and individual sample values (n=96). Three vegetation indices (RSI, NDSI, RDSI) were computed and Generalised Additive Models (GAM) were applied to portray the relationship between measured grass biomass and VIs. The highest explanatory power for both modelling techniques was found with RSI and NDSI indices with averaged plot level values having the highest performance (D2 = 0.79, RMSE = 40.15 g/m2), with the band combination of B78 and B43 (908 nm / 667 nm). The best performing vegetation index (RSI) was used to predict grass biomass in the study area, which indicated a biomass range of 0 to 2894 g/m2. The study highlights the potential of using hyperspectral imagery to assess grass biomass in the savanna environments. However, challenges and limitations were faced related to the heterogeneous nature of savannas, varying weather conditions affected by rainfall, the temporal limits of the study, and disturbances in spectral information caused by heavily grazed areas, dead material, and preprocessing techniques. It is suggested that future research considers these factors by incorporating a broader set of variables, extending the duration of the study, exploring various preprocessing techniques, increasing the sample size, and employing additional data sources, such as active sensors and hyperspectral satellite imagery, to enhance model performance and improve accuracy.
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