Browsing by Subject "NDVI"

The world population is growing and is expected to reach over 9 billion in about 30 years. Climate change is also widely expected to worsen famines in certain regions of the world. This will drastically increase global food demand. Food security efforts should be therefore be geared towards promoting food crops that can thrive in these regions and can withstand the condition likely to be brought about by changing climate. Cassava is a typical example of such a crop. This study investigated the use of digital images to estimate growth parameters of young cassava plants. Cassava was cultivated in pots at the University of Helsinki greenhouse at Viikki. The plants were given different water level (100%, 60% and 30% saturation) and potassium (0.1, 1.0, 4.0, 16.0 and 32.0mM) treatments. Digital red-green-blue (RGB) and multispectral images were taken every other week for 5 consecutive times. The images were processed to obtain leaf area, Normalized Difference Vegetation Index (NDVI), and Crop Senescence Index (CSI) and correlated with directly measured growth parameters of the young cassava crops. It was observed that leaf area that was computed from images, and NDVI which was computed from the multispectral images have significant positive correlations with the growth parameters, ie, actual leaf area, chlorophyll content, and plant biomass. CSI however showed weak a correlation between the growth parameters of the young cassava plants. Images leaf area and NDVI were then used to identify the changes in the effects of the water and potassium treatments.

Due to the harsh conditions in high latitude alpine and arctic regions, climate or land use changes make them very vulnerable. Thus, it is vital to study the habitats of these regions and increase our understanding of what factors impact species distributions. Species distribution modelling can be used to predict possible habitats for species and further inspect the relationships between different environmental variables and species. Generally, these species distribution models have been created using variables describing the topographical and climatic conditions of the study area. Recently there has been more evidence supporting the inclusion of biotic variables to species distribution models at all scales. Including biotic variables can be difficult, as these relationships can be challenging to quantify. This study uses the Normalized Difference Vegetation Index (NDVI) as a surrogate for plant biomass, thus representing biotic interactions. This study aims to answer what are the relationships between environmental variables and the predicted distributions and will including a biotic variable improve the species distribution models. The study data includes observational data from 683 arctic and alpine plant species from Norway, Sweden, and Finland. The observation data were collected from the three national databanks of Norway, Sweden and Finland and completed with observations from the Global Biodiversity Information Facility and observation data collected by the BioGeoClimate Modelling Lab. The cohesive study area was outlined with the biogeographical regions defined by the European Environment Agency. Overall, six environmental variables are used in this study: annual mean temperature, the maximum temperature of the warmest month, annual precipitation, elevation difference in a cell, bedrock class, and NDVI. The NDVI data was gathered by NASA’s MODIS sensors. The observations and the environmental variables were projected into a grid consisting of 1 x 1 km cells covering the whole study area. This study uses the ensemble modelling technique with four individual modelling methods: generalized linear models (GLM), generalized additive models (GAM), generalized boosted models (GBM) and random forests (RF). The modelling process consisted of two modelling rounds so that the impact of NDVI could be evaluated. The first modelling round included all the environmental variables except NDVI (the topoclimate model) and the second modelling round included all the environmental variables (the full model). The two temperature variables, annual mean temperature and the maximum temperature of the warmest month, had the highest mean variable importance values. With the topoclimate model, annual precipitation ranked third with the rest of the climate variables, but when NDVI was added to the models, it rose above annual precipitation. Overall, among the studied arctic and alpine species, the variable importance values of both the edaphic and topographical variables were low. In general, both the topoclimate models and full models performed very well. The mean AUC- and TSS-values were all higher for the full models, indicating that including a biotic variable improved the models. When the binary predictions of both modelling rounds were compared, it was clear that NDVI refined the projected distributions for most species. The results from this study confirm the discovery that including a biotic variable, such as NDVI, has the potential to increase the predictive power of species distribution models. One of the main problems with including biotic variables in species distribution models has been the difficulty of quantifying biotic interactions. NDVI can thus be a promising tool to overcome these difficulties, as it is one of the most direct variables to describe ecosystem productivity, can be acquired at various scales, and as remotely sensed data, it can also cover areas that are difficult to access.

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.

Tutkimuksen tavoitteena oli selvittää kasvien stressitekijöiden vaikutuksia kasvin klorofyllifluoresenssiin ja reflektanssiin. Tutkimuksessa havainnoitiin, voidaanko eri bioottisia ja abioottisia stressitekijöitä mitata kasvin erittämästä klorofyllifluoresenssista ja reflektanssista, kun stressattuja lehtiä mitataan spektrofotometrillä. Tarkoitus oli myös selvittää olisiko stressin vaikutusta kasviin mahdollista havaita kasvin erittämässä spektrissä jo ennen kuin stressistä on olemassa paljain silmin näkyviä merkkejä. Kasvien stressiä on mahdollista mitata kasvien ”vihreydellä”, jota pystyy mittaamaan kaukokartoitusmenetelmin satelliiteista ja lentokoneesta käsin. Lehvästötason mittauksissa käytetään hyväksi mm. NDVI-indeksiä (Normalized Difference Vegetation Index), jossa vertaillaan eroja kasvin heijastamassa punaisessa ja infrapunaisessa spektrissä. NDVI-mittausten haittapuolena on kuitenkin niiden hidas reaktiokyky kasvien kokemaan stressiin. Tämän takia on kehitetty sekä klorofyllifluoresenssimenetelmä, että PRI-indeksi, joiden on mahdollista löytää eroja kasvin heijastamassa fluoresenssissa ja reflektanssissa kasvin ollessa pienenkin stressin alaisena. Tässä tutkimuksessa vertaillaan eri reflektanssi-indeksien (PRI, SR, NDVI) ja klorofyllifluoresenssin käyttömahdollisuuksia stressitasojen havaitsemisessa. Tulokset analysoitiin tilastollisin menetelmin käyttäen Spearmanin järjestyskorrelaatiokerrointa ja laskenta suoritettiin R-Studio ja SPSS-ohjelmilla. Aineisto mitattiin neljässä eri kohteessa kesän 2017 aikana. Jokaisessa kohteessa valittiin yhdeksästä kahteentoista koekasvia, joista kustakin mitattiin neljä lehteä. Mitattavien lehtien tuli olla täysikasvuisia, sekä olla puun valoisimmalla puolella. Lehdet mitattiin spektrofotometrillä pimeässä tilassa mittausten standardoimiseksi ja jotta ulkoa tuleva valo ei häiritsi mittauksia. Spektrofotometrillä mitattiin lehden vakaan tason fluoresenssi, sekä lehden reflektanssi. Samoista koepuista mitattiin myös lehtien absorbanssia, valon läpäisevyyttä, kaasujenvaihtoa, sekä maksimikvanttisaantoa. Klorofyllifluoresenssin 685nm ja 740nm taajuusalueiden suhteella kyettiin havainnoimaan typen määrää lehdissä ja PRI-indeksin huomattiin olevan hyvä työkalu natriumin määrän havainnoimiseen kasvien lehdissä. Lehtien stressin määrän huomattiin joissain koetilanteissa korreloivan fotosynteesin määrän kanssa jo stressin ollessa hyvin pientä. Tulokset indikoivat, että klorofyllifluoresenssin mittaaminen olisi varteenotettava menetelmä kasvien hyvinvoinnin mittaamiseen jo ennen kuin kasvissa on paljain silmin nähtäviä merkkejä stressistä. Tässä tutkimuksessa tehdyt havainnot pätevät kasveissa vain lehtitasolla, mutta lisätutkimuksissa voisi selvitä, voiko tuloksia yleistää myös kasvi- ja lehvästötasolle.

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.

As a response to Arctic climate warming, Arctic vegetation is changing. Studies have shown a greening trend both in satellite-derived data (spectral greening) and in field measurements (vegetation greening). Some studies indicate that the increase in vegetation productivity in the Arctic is one of the drivers of the increase in organic carbon in lakes, lake brownification. The aim of this thesis is to bring more knowledge about this possible connection between vegetation change and lake brownification in a circumpolar context. My two main research questions are: 1) How has the summer terrestrial NDVI value, in short, the normalized vegetation index to estimate the amount of green vegetation, changed during the past 20 years in the study areas and is there variation between lake groups? and 2) How has the amount of organic carbon changed in the studied lakes? These questions are complemented by the following sub-questions: Have summer temperature or precipitation affected NDVI change? What is the relationship between summer NDVI and lake organic carbon, and between the change in temperature/precipitation and the change in lake organic carbon? I conducted a circumpolar study about multi-decadal greening and climate trends around 15 Arctic and subarctic lakes divided into five groups, using satellite-derived vegetation data (NDVI). I then compared these data with sediment data to study if potential greening has a connection to the lakes’ total organic carbon load. The study areas are located in South-Central Canada, Alaska, North Finland, Russia – Yakutia and Russia – Chukotka. I found that summer surface air temperature has increased significantly in all of the study areas during 1961-2018. Total summer precipitation has not significantly changed during the study period, but the trend has been positive in all study areas except for Russia, Chukotka. Winter precipitation has significantly decreased in Alaska, and in both of the Russian areas, and significantly increased in Jänkäjärvi, Finland during 1961-2018. NDVI has increased significantly in Jänkäjärvi (Finland), Rauchuagytgyn and Illerney (Russia, Chukotka) during 2000-2021. In these lakes the environmental variables were affecting NDVI. The connection between NDVI in the study areas and lake TOC was positive, but not statistically significant. The situation was the same for the comparison between temperature and TOC for most of the lakes. When comparing the change in summer and winter precipitation and the change in TOC, for most of the lakes, precipitation had decreased while TOC increased. In conclusion, the results indicate that environmental conditions are changing in the study areas and that in some areas that has led to an increase in summer terrestrial NDVI values. It may also be that catchment area greening can increase lake TOC. However, this connection needs more research, for instance with a larger sample size. This study with its varying results also supports the notion of heterogeneous Arctic environments.

Global population growth and the loss of arable land cause pressure for improving the effectiveness of crop cultivation. Meanwhile, from an environmental perspective, there are good grounds for determining the use of fertilizer and pesticides based on crop yield potential. While radiometric measurements have been used for long in remote sensing, the approach is also suitable for carrying out smaller-scale investigations. A measurement approach based on the differences in two light wavelength reflections could provide a means to acquire current data on the state of canopy development. The measurements could be used as the basis for allocating cultivation investments to enable optimal yield response. The aim of this study was to examine whether Normalized Difference Vegetation Index (NDVI) measurements conducted during the growing season could be used to assess spatial variation in vegetation and protein content in the crop yield. The field test was implemented as a strip-plot test in Eastern Uusimaa, Finland, during the growing season 2011. The used cultivated plant was spring wheat (Triticum aestivum L. ‘Amaretto’) and the test comprised four nitrogen fertilizer and three seed density test plots. The NDVI values of the plots were measured once per week using a measurement device attached to a tractor. In total, the measurements were conducted nine times. SPAD values were also determined once during the growing season. The growing season was initially unseasonably warm with little precipitation. Spray irrigation was used to ensure that the test would not be impaired by stress caused by drought. Three square-shaped areas from the test plots were threshed, and their crop amount, protein content, standard mass and weight of one thousand kernels was determined. A positive correlation between the NDVI value of the fertilizer test plots and harvested grain increased as the wheat canopy grew and was highest during the flag leaf stage. There was no equally clear correlation between crop quality characteristics and the NDVI values. NDVI has potential use in precision agriculture in assessing spatial variation in wheat canopy during the growing season, and targeting additional fertilizer and plant protection measures based on the obtained data.