Differentiation of plant age in grasses using remote sensing
Knox, N. ; Skidmore, A.K. ; Werff, H.M.A. van der; Groen, T.A. ; Boer, W.F. de; Prins, H.H.T. ; Kohi, E. ; Peel, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 24 (2013)10. - ISSN 0303-2434 - p. 54 - 62.
difference water index - monitoring vegetation - nitrogen concentration - imaging spectroscopy - hyperspectral data - boreal regions - time-series - green-up - phenology - reflectance
Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems.
Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices
Hamzeh, S. ; Naseri, A.A. ; Alavi Panah, S.K. ; Mojaradi, B. ; Bartholomeus, H. ; Clevers, J.G.P.W. ; Behzad, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 282 - 290.
salt-affected soils - difference water index - spectral reflectance - precision agriculture - chlorophyll content - canopy reflectance - plant-leaves - fresh-water - hyperion - leaf
The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.