Browsing by Subject "NDVI"

Crop monitoring in commercial indoor farming is a commonly used method in assessing the general productivity of the cultivated plants. This assessment practice is typically conducted manually by greenhouse workers and is sometimes supplemented by certain hand-held or stationary devices. An interesting example of novel device-assisted crop monitoring technologies utilizes digital imaging devices and computer-driven image analysis algorithms that have been prominently employed within the field of plant phenotyping. In the context of botanical studies, they have been used in e.g. characterizing various complex interactions between the genotypes of important food crops and their agronomic traits in specific prevailing environmental conditions. Additionally, image-based data acquisition technologies also present very interesting prospects for precision agriculture management practices. They could be harnessed to scan entire greenhouse compartments continuously and acquire massive amounts of data on multiple morphological and physiological aspects of crop growth and development in a non-destructive fashion. The acquired data could be implemented into mathematical greenhouse control models and utilized in a plethora of useful applications, including e.g. estimating and predicting biomass production and yield, detecting and localizing potential abiotic/biotic stress symptoms at an early stage, and ultimately enhancing overall crop production efficiency. In this thesis, these imaging technologies were explored in practice by designing and constructing a growth chamber embedded with automatic climate control and a low-cost multispectral imaging subsystem. The final assembly was tested by conducting a simple experiment involving drought-stressed sweet basil plants (Ocimum basilicum L. cv. ‘Genovese’) to determine how early drought-stress related symptoms could be detected purely from multispectral images. While the system carried out the tasks of automated climate control and continuous image capture adequately, the implemented approach in drought-stress detection was deemed unsuccessful. Significant differences between drought-stressed plants and their respective controls were not observed until visible symptoms were present. This was assumed to be due to incompatibility of the camera module’s spectral sensitivity in detecting changes in water content in plant tissue.

Remote sensing brings new potential to complement environmental sampling and measuring traditionally conducted in the field. Satellite images can bring spatial coverages and accurately repeated time-series data collection to a whole new level. While developing methos for doing ecological assessment from space in situ sampling is still in key role. Satellite images of relatively coarser pixel size where individual plants or trees are not possible to separate usually utilize vegetation indices as proxies for environmental qualities and measures. One of the most extensively used and studied vegetation index is Natural Difference Vegetation Index (NDVI). It is calculated as normalized ratio between red light and near-infra-red radiation with formula: NDVI=NIR- RED/NIR+RED. Index functions as a measure for plant productivity, that has also been linked to species-level diversity. In this thesis MODIS NDVI (MOD13Q1, 250 m x 250 m resolution) and selected additional variables were examined through their predictive power for explaining variation in tree species richness in six different types of moist tropical evergreen forests in the province of West Kalimantan, on the island Borneo in Indonesia. Simple and multiple regression models were built and tested with main focus on 20- year mean-NDVI. Additional variables used were aboveground carbon, elevation stem count, tree height and DBH. Additional variables were examined initially on individual basis and subsequently potential variables were then combined with NDVI. Results indicate statistically significant, but not very strong predictable power for NDVI (R2=0.25, p-value=2.11e-07). Elevation and number of stems outperformed NDVI in regression analyses (R2=0.64, p-value=2.2e-16 and R2=0.36, p-value=4.5e-11, respectively). Aboveground biomass carbon explained 19% of the variation in tree species richness (p-value=6.136e-06) and thus was the worst predictor selected for multiple regression models. Tree height (R2=0.062, p-value=0.0137) and DBH (R2=0.003, p-value=0.6101) did not show any potential in predicting tree species richness. Best variable combination was NDVI, elevation and stem count (R2=0.71, p-value=2.2e-16). Second best was NDVI, elevation and aboveground biomass carbon (R2=0.642, p-value=2.2e-16), which did not promote for biomass carbon as a potential predictor as model including only NDVI and elevation resulted nearly identically (R2=0.639, p-value=2.2e-16). Model including NDVI and stem count explained 54% of the variation in tree species richness (p-value=2.2e-16) suggesting elevation and stem count being potential variables combined with NDVI for this type of analysis. Problems with MODIS NDVI are mostly linked to the relatively coarse spectral scale which seems to be too coarse for predicting tree species richness. Spectral scale also caused spatial mismatch with field plots as being significantly of different sizes. Applicability in other areas is also limited due to the narrow ecosystem spectrum covered as only tropical evergreen forests were included in this study. For future research higher resolution satellite data is a relevant update. In terms methodology, alternative approach known as Spectral Variability Hypothesis (SVH), which takes into account heterogeneity in spectral reflectance, seems more appropriate method for relating spectral signals to tree species richness.