This thesis deals with the influence of foliar application of selected forms of zinc on the yield and quality of corn grain (Zea mays L.). The issue was dealt with in the form of a small-plot vegetation experiment on the landfill site in Žabčice. Zinc was applied in the 6-8 leaf phase in three forms of ZnO, ZnSO4 and Zn-EDTA at doses of 100, 250 and 500 g.ha-1 in each of them. A zinc-free variant was used as a control. In the course of vegetation (14 days after fertilizer application (20 June 2018)) and after harvesting, an inorganic analysis of the plants (Zn content) was carried out, NDVI and NDRE index were determined during vegetation. After harvest, the yield, starch content and weight of a thousand seeds were determined. Zinc fertilization proved to be (p≤0.05) affected by the zinc content during vegetation, but had no effect on NDVI and NDRE index. Grain yield was positively influenced, although no proven zinc effect was demonstrated. WTS and starch content in the grain were neither demonstrably affected nor significantly different from control. Zinc content in the grain was not significantly affected, but zinc fertilization was positive.

Different type of images provided by various combinations of cameras have the power to help increase and optimize plant growth. Along with a powerful deep learning model, for the purpose of detecting these stress indicators in RGB images, can significantly increase the harvest yield. The field of AI solutions in agriculture is not vastly explored and this thesis aims to take a first step in helping explore different techniques to detect early plant stress. Within this work, different types and combinations of camera modules will initially be reviewed and evaluated based on the amount of information they provide. Using the chosen cameras, we manually set up datasets and annotations, chose and then trained a suitable and appropriate algorithm to predict deviations from an ideal state in plant production. The algorithm chosen was Faster RCNN, which resulted in having a very high detection accuracy. Along with the main type of cameras, a new particular type of images analysis, named SI-NDVI, is done using a particular combination of the main three cameras and the results show that it is able to detect vegetation and able to predict or show if a plant is stressed or not. An in-depth research is done on all these techniques to create a good quality dataset for the purpose of early stress detection. / Olika typer av bilder försedda av olika kombinationer av kameror har kapaciteten att hjälpa öka och optimera odling av växter. Tillsammans med en kraftfull deep learning-modell, för att detektera olika stressindikatorer i RGB bilder, kan signifikant öka skördar. Fältet av AI-lösningar inom jordbruk är inte väl utforskat och denna uppsats siktar på att ta ett första steg i utforskandet av olika tekniker för att detektera tidig stress hos växter. Inom detta arbete kommer olika typer och kombinationer av kameramoduler bli utvärderade baserat på hur mycket information de kan förse. Genom att använda de valda kamerorna skapar vi själva dataseten och kategoriserar dem, därefter välja och träna en lämplig algoritm för att förutspå förändringar från ett idealt tillstånd för växtens tillväxt. Algoritmen som valdes var Faster RCNN, vilken hade en väldigt hög träffsäkerhet. Parallellt med de huvudsakliga kamerorna genomförs en ny typ av bildanalys vid namn SI-NDVI genom användandet av en särskild kombination av de tre kameror och resultat visar att det är möjligt att detektera vegetation och förutspå eller visa om en växt är stressad eller inte. En fördjupad undersökning genomförs på alla dessa tekniker för att skapa ett dataset av god kvalité för att kunna förutspå tidig stress.

Detta arbete utvärderar ett kamerasystem som kan ta bilder för analys av växters välmående. Kamerasystemet bygger på en Raspberry pi model 3 b+, en PInoIR kamera modul v2 och ett blått filter. Kameran kan övervaka växter genom att den inte har något infrarött filter. Då kan matematiska uträkningar utföras på allt kamerans sensorer kan ta in. Ett experiment har designats för att skapa mätvärden som används i utvärdering av kamerans funktionalitet. Kamerasystemet bedöms fungera enligt det förespråkade ramverket och den programstack det bygger på. Det analyserade resultatet visar att kamerasystemet kan se skillnad på hydroponiska odlingar med olika tillgång till näring. / This work evaluates a camera system that can take pictures for the analysis of plants' well-being. The camera system is based on a Raspberry pi model 3 b+, a PInoIR camera module v2 and a blue filter. The camera can monitor plants because it does not have an infrared filter. This enables mathematical calculations to be performed on light registered by the camera's sensors. An experiment has been designed to create metrics that are used in evaluating the camera's functionality. The camera system is preforming well by using the proposed framework and program stack. The analyzed results show that the camera system is able to spot the difference between hydroponic cultures with access to differents amounts of nutrition.

Groundwater Dependent Ecosystems (GDEs) concentrate high levels of biodiversity and several species not found anywhere else. They prevail in the landscape through the ecological contribution of groundwater. They, GDEs, are vulnerable to drastic changes in groundwater depth. If, for example, bulk groundwater pumping drastically increases the groundwater depth and GDEs can no longer access it, they would die out. In the Cape Floristic Region (CFR), South Africa, there is limited information about the spatial distribution of groundwater dependent ecosystems. With the CFR having multiple locations with current and subsequent bulk groundwater pumping, identifying the spatial distribution of GDEs is a prerequisite for establishing their groundwater requirements. This dissertation presents a proposed novel method to identify rapid recovering wetlands predicted to be GDEs and uses Random Forest (RF) to predict their spatial distribution. The proposed novel approach leveraged the periodic fire disturbances in the CFR and applied the remote sensing index; Normalised Difference Vegetation Index (NDVI) extracted from high spatial resolution (1 m) aerial orthoimages. The proposed novel approach involves three levels of analysis. The first two levels used a one-way Analysis of Variance (ANOVA) to analyse the sensitivity of mean NDVI to discriminate wetland and non-wetland classes in burned and unburned study sites, and a post-hoc test: Tukey's Honest Significant Differences (HSD) pair-wise comparison to detect differences between the wetland and non-wetland mean NDVI and infer an NDVI threshold of wetland classes. In unburned sites, ANOVAshowed no statistical significance between wetland and non-wetland classes, F (2,15) = 3.53, p = 0.055. In burned sites, however, ANOVA showed there was a significant difference between wetland and non-wetland classes, F (2,15) = 9.66, p = 0.002. ANOVA and Tukey showed there were significant differences betweenwetland and non-wetland classes, with wetlands having between 0.22 and 0.37 greater NDVI than non-wetlands. The last level of analysis employed a kernel density estimator function to assess the recovery rate post-burn and use it to detect faster recovery as potential of wetlands to be GDEs; results showed that potential wetland GDEs experience rapid NDVI recovery > 236 days post-fire. In the fire prone CFR, leveraging fire data to detect GDEs provides a potentially simple and efficient way of building a local database for GDEs. The proposed novel approach showed leveraging fire data is a simple alternative to laborious field data to identify and map GDEs in the CFR. But because of the finite spectral bands in aerial orthoimages, Sentinel-2A multi-epochs dataset was utilised to carry out random forest for predicting the spatial distribution of potential wetland GDEs in the Kogelberg Nature Reserve. Sentinel-2A bands: Short-Wave Infrared (SWIR), NearInfrared (NIR), Red-edge, Red, Green, NDVI and Normalised Difference Wetness Index (NDWI) predictors and the potential wetland GDEs/non-wetland classes as the response. I tuned RF using five-fold repeated spatial cross-validation instead of the typical cross-validation tuning to account for the spatial structure of the data. The overall predictive accuracy of RF was between 59%-71%. This predictive accuracy may have been reduced by the application of spatial cross-validation that accounted for the spatial autocorrelation in the multi-date data. The dissertation showed that Sentinel-2A multi-date data applies in predicting the distribution of potential wetland GDEs but might not be effective for smaller (< 100 m2) wetlands. These small wetlands showed rapid post-fire recovery (less than a year post-fire) and were effectively detected with high resolution aerial orthoimages (1 m) spatial resolution.

No description available.

Student Number : 9511039F - MSc Dissertation - School of Animal, Plant and Environmental Sciences - Faculty of Science / An estimated 91 % of South Africa’s total land area is considered dryland and susceptible to desertification. In response, South Africa has prepared a National Action Programme to combat land degradation, and this requires assessment and monitoring to be conducted in a systematic, cost effective, objective, timely and geographically-accurate way. Despite a perception-based assessment of land degradation conducted in 1999, and a land-cover mapping exercise conducted for 2000/2001, there are few national scientifically rigorous degradation monitoring activities being undertaken, due largely to a lack of objective, quantitative methods for use in large-scale assessments. This study therefore tests a satellitederived index of degradation for the Limpopo Province in South Africa, which is perceived to be one of the most degraded provinces in the country. The long-term average maximum normalized difference vegetation index (NDVI), calculated from a time series (1985-2004) of NOAA AVHRR satellite images, as a proxy for vegetation productivity, was related to water balance datasets of mean annual precipitation (MAP) and growth days index (GDI), using both linear and non-linear functions. Although the linear regressions were highly significant (p<0.005), a non-linear four parameter Gompertz curve was shown to fit the data more accurately. The curve explained only a little of the variance in the data in the relationship between NDVI and GDI, and so GDI was excluded from further analysis. All pixels that fell below a range of threshold standard deviations less than the fitted curve were deemed to represent degraded areas, where productivity was less than the predicted value. The results were compared qualitatively to existing spatial datasets. A large proportion of the degraded areas that were mapped using the approach outlined above occurred on areas of untransformed savanna and dryland cultivation. However the optical properties of dark igneous derived soils with high proportions of smectitic minerals and therefore low reflectance, were shown to lower NDVI values substantially. Overall, there was an acceptable agreement between the mapped degradation and the validation datasets. While further refinement of the methodology is necessary, including a rigorous field-based resource condition assessment for validation purposes, and research into the biophysical effects on the NDVI values, the methodology shows promise for regional assessment in South Africa.

Microbial life on Mars is not visible from orbit or by rover cameras, but the fracture networks and scarp morphologies associated with fractures they could live in are measurable. We conducted a field analogue study of 92 normal fault scarps in the Sheepshead Mountains, Oregon to examine the correlation between scarp morphology and vegetation growth in the Steens Basalt. While vegetation is not expected on Mars, the fracture networks that sustain vegetation offer the same micro-environment that would support and protect endoliths. Structural variables were measured in the field and infrared spectra of fault scarps were measured using a handheld multispectral camera and vegetation indices were calculated from these images. Statistical analysis of the scarp morphologic parameters indicate that interconnectedness of fractures is key for elevated vegetation and is represented by a range of parameters. Results support a model for ideal slopes to investigate for preserved biological activity on Mars.

The growth patterns of emergent vegetation can be a useful indicator for factors affecting lake health. However, field data to characterize emergent vegetation at many reservoirs may not be available or may be limited to small, isolated areas. We present a case study using remotely sensed data from the Landsat satellite to generate data to represent emergent vegetation in the near-shoreline and tributary delta areas of Malheur Lake, Oregon. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area on the day of the satellite collection, average daily maximum temperatures for April, May, June, and July, and average daily precipitation in June, all parameters that could affect vegetation. To estimate historic emergent vegetation extent, we computed the Normalized Difference Vegetation Index (NDVI) for 30 years of Landsat satellite images from 1984 to 2013. Around Malheur Lake we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas). For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels with NDVI values greater than 0.2. We measured lake area by computing the Modified Normalized Difference Water Index (MNDWI) and computing the area by summing the pixels that indicated water. We compared NDVI time-series values with the time series for lake area, June precipitation, and maximum daily temperatures for April, May, June, and July to determine if these parameters were correlated. Correlation would imply that emergent vegetation was influenced by the parameter. We found that correlations of vegetative extent in any of the eight ROIs with the selected parameters were minimal, indicating that there are other factors besides the ones chosen that drive emergent vegetation levels in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail for quantification and description of ecosystem changes and thus offer a good source of information to understand historic trends in reservoir health. We expect that future work will explore other potential drivers for emergent vegetation extent, such as carp populations in Malheur Lake which are known to affect emergent vegetation. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.

Human activities are a major driver of biodiversity degradation and loss, especially in tropical forest areas, where forest-fringe towns and villages depend on the forests for their livelihoods. In order to reduce threats that human activities pose to biodiversity, livelihoods support programs are employed as economic incentives for biodiversity conservation. These programs support the livelihoods activities of local communities, with the aim of triggering favorable attitudes and behaviors towards conservation, and ultimately reduce biodiversity degradation. Their effectiveness as conservation tools has not been evaluated. I investigated the effects of livelihoods programs on conservation attitudes and the consequent effects on biodiversity in the Afadjato-Agumatsa and Atewa forest areas in southeastern Ghana. The study areas are coupled human and natural systems, which are excellent for research in the theoretical framework of biocomplexity in the environment. Using literature reviews and field visits, I documented the specific livelihoods support activities (LSAs) used for biodiversity conservation, their historical trend and geographical distribution in Ghana. I used ex-post costbenefit analysis to determine socio-economic estimates of the LSAs in the two forest areas. Since communities were not randomly assigned to the interventions, I employed quasi-experimental design to evaluate the effects of LSAs on environmental attitudes. I evaluated the effect of conservation attitudes on biodiversity at two levels. These levels included 1) functional biodiversity at the landscape level represented by mean Normalized Difference Vegetation Index (NDVI) of forest; and 2) compositional biodiversity at the species level represented by species diversity of fruit bats. iv The earliest record of LSAs used for biodiversity conservation in Ghana was in 1993. I identified 71 different activities belonging to eight categories. Some of these activities are beekeeping, animal husbandry, crop farming, and snail rearing. Most LSA programs have been in northern Ghana. There was an increasing tendency to make LSAs part of every conservation program in Ghana and this satisfies the current policy of collaborative conservation. The socio-economic estimates of LSAs included: 1) capital investment; 2) net socio-economic benefits; and 3) the benefit-cost ratio. The per-community values of the three estimates were not different between the two study areas. The per capita values of capital investment and net economic benefit were not significantly different between the two study areas. However, benefitcost ratio per capita was higher in Afadjato-Agumatsa than in Atewa. Estimates of economic returns from LSAs were marginal but the perceptions of success were relatively high. Environmental attitudes in LSA communities and non-LSA communities were not significantly different, and this was confirmed by an estimate of infinitesimal effects of LSAs on forest conservation attitudes. Among LSA communities, benefit-cost ratio of LSAs predicted favorable forest conservation attitudes; and change in pro-conservation attitudes were significantly higher in communities that had active LSAs than in communities which had no active LSA. Mean NDVI of the forests decreased from 1991 to 2000 and decreased further but at a slower rate to 2010. Higher forest conservation attitudes predicted higher mean NDVI in 2010. Higher change in mean NDVI from 1991 to 2000 predicted higher change in mean NDVI from 2000 to 2010. Eleven of the 13 fruit bat species in Ghana were recorded in the study areas. Longer v distances between a local community and its forest predicted higher species diversity of forestspecialist fruit bats. The results indicate that LSAs have become a major contribution to Ghana’s current collaborative forest policy. The fact that perceptions of LSA success were moderate even though the economic returns from them were marginal suggest that other factors such as provision of employment, training in new skills and community cohesion played a part in how communities viewed the success as LSAs. Evaluations of conservation attitudes suggest that just participating in LSAs did not improve attitudes; but higher benefit-cost ratio predicted favorable conservation attitudes, and conservation attitudes were higher in communities that sustained their LSAs. Therefore, it may serve biodiversity conservation to invest in LSAs that can be sustained and involve the least costs to local communities. Primary production of the forests, a proxy for a functional habitat, continued to decrease. Preventing communities from locating closer to forests could improve fruit bat diversity, which contributes to natural forest regeneration. Improving conservation attitudes should be an objective of conservation at the landscape scale. On the basis of the results, I developed a conceptual model for forest biodiversity conservation in a biocomplexity framework. This model could be useful for evaluating conservation in tropical forest areas. Lessons from this study can be applied in other incentive-based conservation programs such as payments for ecosystem services systems and carbon market schemes. I suggest that this study be repeated after a decade and that other socio-political and biogeochemical variables be integrated into future studies.

Over the past couple of decades, fires have experienced changes on a global scale. These changing fire regimes point to an alarming direction where fire-dependent ecosystems are experiencing a decline in burned area, while fire-independent ecosystems are experiencing an increase. As a result, land cover change is seen in both types of ecosystems where the native plant communities run the risk of disappearing, and recovery becomes increasingly important. One of the areas experiencing a notable increase in fires is California, US. Here, both observed and projected changes indicate increasing frequency of fires, fire size and fire severity. In this study, post-fire recovery for 5 land cover types in California is compared using Normalized Difference Vegetation Index (NDVI) time-series. Two metrics are used for post-fire recovery, where a metric that describes short-term recovery is found most appropriate for a comparison between land cover types. It is found that the land cover type “Trees” has the longest recovery, followed by “Herbaceous/Shrubs”. Faster recovery times are found in the late fire season compared to the early fire season, indicating an influence of precipitation on post-fire vegetation recovery. Similarly, faster recovery times are found in a semi-arid climate zone compared to the Mediterranean climate zones. This indicates the potential influence of species composition on post-fire vegetation recovery. Results particularly show differences in post-fire recovery between land cover types, but also between fire seasons and climate zones. To examine these details in further detail, fire severity, meteorological data, and a more detailed classification for vegetation types could be implemented as factors determining post-fire recovery.