Your search found 10 records
1 Gitelson, A. A.; Merzlyak, M. N. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5):689-692.
(Location: IWMI-HQ Call no: P 7484 Record No: H038158)
2 Moreau, J. (Ed.) 1997. Advances in the ecology of Lake Kariba. Harare, Zimbabwe: University of Zimbabwe Publications. 271p.
Lakes ; Ecology ; Ecosystems ; Hydrology ; Water quality ; Nutrients ; Chlorophylls ; Nitrogen fixation ; Phytoplankton ; Zooplankton ; Invertebrates ; Aquatic environment ; Fisheries / Africa / Zimbabwe / Zambia / Zambezi River / Lake Kariba
(Location: IWMI-HQ Call no: 577.63 G100 MOR Record No: H039342)
3 Finlayson, Max; Gillies, J. C. 1982. Biological and physicochemical characteristics of the Ross River Dam, Townsville. Australian Journal of Marine and Freshwater Research, 33:811-827.
Dams ; Lakes ; Salvinia molesta ; Aquatic weeds ; Surveys ; Phytoplankton ; Chlorophylls ; Fish ; Water quality ; Nutrients / Australia / Queensland / Townsville / Ross River Dam
(Location: IWMI-HQ Call no: P 7754 Record No: H039704)
4 Prasad, M. B. K.; Maddox, M. C.; Sood, Aditya; Kaushal, S. 2014. Nutrients, chlorophyll and biotic metrics in the Rappahannock River-Estuary: implications of urbanization in the Chesapeake Bay Watershed, USA. Marine and Freshwater Research, 65:475-485. [doi: https://doi.org/10.1071/MF12351]
Watersheds ; Rivers ; Water quality ; Ecosystems ; Nutrients ; Chlorophylls ; Urbanization ; Land use / USA / Rappahannock River-Estuary / Chesapeake Bay Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H046377)
(0.83 MB)
In the Chesapeake Bay watershed, various endeavors such as the inter-state agreements and Chesapeake 2000 agreement have been implemented to improve water quality and ecological conditions which have produced mixed results at best in various tributaries. In order to evaluate the management efforts on ecological conditions in the Rappahannock River watershed, we analyzed the long-term variability in land-use, nutrient content, and ecological biotic metrics. It appears that the interannual variability in nutrient loadings and concentrations are largely influenced by changes in urbanization and climate. Significant increases in urban development (35%) and population growth have exacerbated both point and non-point nutrient pollution in the Rappahannock River. Comparatively low N:P ratio in the tidal zone than the non-tidal zone may be due to salinity induced phosphorus leaching from sediments regulating the water quality along the river-estuary continuum. In addition, interannual variability in ecological biotic metrics demonstrates degrading ecological conditions in the Rappahannock River watershed, which are primarily due to increasing watershed urbanization driving high nutrient loadings and altered nutrient stoichiometry.
5 Chen, Y.; Takara, K.; Cluckie, I. D.; de Smedt, F. H. 2004. GIS and remote sensing in hydrology, water resources and environment. Wallingford, UK: International Association of Hydrological Sciences (IAHS). 422p. (IAHS Publication 289)
GIS ; Remote sensing ; Hydrology ; Water resources ; Water management ; Environmental effects ; Flood control ; Flood plains ; Models ; Forecasting ; Watersheds ; Stream flow ; River basins ; Reservoirs ; Catchment areas ; Lakes ; Land use ; Water power ; Dams ; Management information systems ; Wetlands ; Water quality ; Coastal waters ; Ecology ; Pollutant load ; Runoff ; Sediment ; Precipitation ; Desertification ; Soil erosion ; Urban areas ; Irrigation ; Phytoplankton ; Chlorophylls ; Evapotranspiration ; Carbon / China / Netherlands / Croatia / Hungary / Malaysia / USA / England / Wales / Feilaixia Reservoir / Guangdong / Wei River Basin / Upper Yellow River / Meuse Basin / Drava River / Mura River / Pearl River Delta / Taihu Basin / Danube River Basin / Longyangxia Reservoir / Everglades / Three Gorges Area
(Location: IWMI HQ Call no: 526.0285 G000 CHE Record No: H046621)
(0.41 MB)
6 Gago, J.; Douthe, C.; Coopman, R. E.; Gallego, P. P.; Ribas-Carbo, M.; Flexas, J.; Escalona, J.; Medrano, H. 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153:9-19. [doi: https://doi.org/10.1016/j.agwat.2015.01.020]
Water stress ; Water management ; Water use efficiency ; Sustainable agriculture ; Aerial photography ; Thermography ; Remote sensing ; Precision agriculture ; Crops ; Plant physiology ; Plant water relations ; Canopy ; Reflectance ; Chlorophylls ; Fluorescence
(Location: IWMI HQ Call no: e-copy only Record No: H047412)
(2.14 MB)
Unmanned aerial vehicles (UAVs) present an exciting opportunity to monitor crop fields with high spatial and temporal resolution remote sensing capable of improving water stress management in agriculture. In this study, we reviewed the application of different types of UAVs using different remote sensors and compared their performance with ground-truth plant data. Several reflectance indices, such as NDVI, TCARI/OSAVI and PRInorm obtained from UAVs have shown positive correlations related to water stress indicators such as water potential (_ ) and stomatal conductance (gs). Nevertheless, they have performed differently in diverse crops; thus, their uses and applications are also discussed in this study. Thermal imagery is also a common remote sensing technology used to assess water stress in plants, via thermal indices (calculated using artificial surfaces as references), estimates of the difference between canopy and air temperature, and even canopy conductance estimates derived from leaf energy balance models. These indices have shown a great potential to determine field stress heterogeneity using unmanned aerial platforms. It has also been proposed that chlorophyll fluorescence could be an even better indicator of plant photosynthesis and water use efficiency under water stress. Therefore, developing systems and methodologies to easily retrieve fluorescence from UAVs should be a priority for the near future. After a decade of work with UAVs, recently emerging technologies have developed more user-friendly aerial platforms, such as the multi-copters, which offer industry, science, and society new opportunities. Their use as high-throughput phenotyping platforms for real field conditions and also for water stress management increasing temporal and resolution scales could improve our capacity to determine important crop traits such as yield or stress tolerance for breeding purposes.
7 Romero, J. M.; Cordon, G. B.; Lagorio, M. G. 2020. Re-absorption and scattering of chlorophyll fluorescence in canopies: a revised approach. Remote Sensing of Environment, 246:111860. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111860]
Plant physiology ; Chlorophylls ; Fluorescence emission spectroscopy ; Canopy ; Vegetation ; Crops ; Peas ; Maize ; Lolium ; Soils ; Remote sensing ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H049717)
(4.08 MB)
The measurement of chlorophyll fluorescence in remote way represents a tool that is becoming increasingly important in relation to the diagnosis of plant health and carbon budget on the planet. However, the detection of this emission is severely affected by distortions, due to processes of light re-absorption both in the leaf and in the canopy. Even though some advances have been made to correct the signal in the far-red, the whole spectral range needs to be addressed, in order to accurately assess plant physiological state. In 2018, we introduced a model to obtain fluorescence spectra at leaf level, from what was observed at canopy level. In this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment. In addition, multiple scattering between the soil and the canopy, and the fraction of land covered by vegetation have also been taken into consideration. We validate this model upon experimental measures, in three types of crops of agronomic interest (Pea, Rye grass and Maize) with different architecture. Our model accurately predicts both the shape of fluorescence spectra at leaf level from that measured at canopy level and the fluorescence ratio. Furthermore, not only do we eliminate artifacts affecting the spectral shape, but we are also able to calculate the quantum yield of fluorescence corrected for re-absorption, from the experimental quantum yield at canopy level. This represents an advance in the study of these systems because it offers the opportunity to make corrections for both the fluorescence ratio and the intensity of the observed fluorescence.
8 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Naiken, V.; Mabhaudhi, Tafadzwanashe. 2022. Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems. Remote Sensing, 14(3):518. [doi: https://doi.org/10.3390/rs14030518]
Maize ; Chlorophylls ; Plant health ; Forecasting ; Smallholders ; Farming systems ; Precision agriculture ; Machine learning ; Unmanned aerial vehicles / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H050903)
(6.76 MB) (6.76 MB)
Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m-2 , 39 µmol/m-2 , and 61.6 µmol/m-2 , respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m-2 and 69.6 µmol/m-2 , respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.
9 Damm, A.; Cogliati, S.; Colombo, R.; Fritsche, L.; Genangeli, A.; Genesio, L.; Hanus, J.; Peressotti, A.; Rademske, P.; Rascher, U.; Schuettemeyer, D.; Siegmann, B.; Sturm, J.; Miglietta, F. 2022. Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy. Remote Sensing of Environment, 273:112957. (Online first) [doi: https://doi.org/10.1016/j.rse.2022.112957]
Plant water relations ; Leaf water potential ; Canopy ; Remote sensing ; Surface temperature ; Vegetation index ; Chlorophylls ; Fluorescence ; Soil water ; Maize / Italy / Tuscany
(Location: IWMI HQ Call no: e-copy only Record No: H050996)
https://www.sciencedirect.com/science/article/pii/S0034425722000712/pdfft?md5=f358a1acfb0c958d984037b09f412ce7&pid=1-s2.0-S0034425722000712-main.pdf
https://vlibrary.iwmi.org/pdf/H050996.pdf
https://vlibrary.iwmi.org/pdf/H050996.pdf
(10.80 MB) (10.8 MB)
Vegetation responds at varying temporal scales to changing soil water availability. These process dynamics complicate assessments of plant-water relations but also offer various access points to advance understanding of vegetation responses to environmental change. Remote sensing (RS) provides large capacity to quantify sensitive and robust information of vegetation responses and underlying abiotic change driver across observational scales. Retrieved RS based vegetation parameters are often sensitive to various environmental and plant specific factors in addition to the targeted plant response. Further, individual plant responses to water limitation act at different temporal and spatial scales, while RS sampling schemes are often not optimized to assess these dynamics. The combination of these aspects complicates the interpretation of RS parameter when assessing plant-water relations. We consequently aim to advance insight on the sensitivity of physiological, biochemical and structural RS parameter for plant adaptation in response to emerging soil water limitation. We made a field experiment in maize in Tuscany (Central Italy), while irrigation was stopped in some areas of the drip-irrigated field. Within a period of two weeks, we measured the hydraulic and physiological state of maize plants in situ and complemented these detailed measurements with extensive airborne observations (e.g. sun-induced chlorophyll fluorescence (SIF), vegetation indices sensitive for photosynthesis, pigment and water content, land surface temperature). We observe a double response of far-red SIF with a short-term increase after manifestation of soil water limitation and a decrease afterwards. We identify different response times of RS parameter representing different plant adaptation mechanisms ranging from short term responses (e.g. stomatal conductance, photosynthesis) to medium term changes (e.g. pigment decomposition, changing leaf water content). Our study demonstrates complementarity of common and new RS parameter to mechanistically assess the complex cascade of functional, biochemical and structural plant responses to evolving soil water limitation.
10 Bangira, T.; Mutanga, O.; Sibanda, M.; Dube, T.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing grassland productivity attributes: a systematic review. Remote Sensing, 15(8):2043. [doi: https://doi.org/10.3390/rs15082043]
Grasslands ; Productivity ; Prediction ; Remote sensing ; Estimation ; Monitoring ; Techniques ; Ecosystem services ; Leaf area index ; Above ground biomass ; Canopy ; Chlorophylls ; Nitrogen content ; Vegetation index
(Location: IWMI HQ Call no: e-copy only Record No: H051841)
https://www.mdpi.com/2072-4292/15/8/2043/pdf?version=1681347101
https://vlibrary.iwmi.org/pdf/H051841.pdf
https://vlibrary.iwmi.org/pdf/H051841.pdf
(3.26 MB) (3.26 MB)
A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.
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