A broad-band leaf chlorophyll vegetation index at the canopy scale

An assessment of the sensitivity at the canopy scale to leaf chlorophyll concentration of the broad-band chlorophyll vegetation index (CVI) is carried out for a wide range of soils and crops conditions and for different sun zenith angles by the analysis of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT ? SAILH leaf and canopy reflectance model. An optimized version (OCVI) of the CVI is proposed. A single correction factor is incorporated in the OCVI algorithm to take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle. An estimate of the value of the correction factor and of the minimum leaf area index (LAI) value of applicability are given for each considered condition. The results of the analysis of the synthetic dataset indicated that the broad-band CVI index could be used as a leaf chlorophyll estimator for planophile crops in most soil conditions. Results indicated as well that, in principle, a single correction factor incorporated in the OCVI could take into account the different spectral behaviors due to crop and soil types, sensor spectral resolution and scene sun zenith angle

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor

Abstract Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato leaves in different stages, including early or asymptomatic stages. One healthy leaf and three diseased tomato leaves (late blight, target and bacterial spots) were defined into four stages (healthy, asymptomatic, early stage and late stage) and collected from a field. Fifty-seven spectral vegetation indices (SVIs) were calculated in accordance with methods published in previous studies and established in this study. Principal component analysis was conducted to evaluate SVIs. Results revealed six principal components (PCs) whose eigenvalues were greater than 1. SVIs with weight coefficients ranking from 1 to 30 in each selected PC were applied to a K-nearest neighbour for classification. Amongst the examined leaves, the healthy ones had the highest accuracy (100%) and the lowest error rate (0) because of their uniform tissues. Late stage leaves could be distinguished more easily than the two other disease categories caused by similar symptoms on the multi-diseased leaves. Further work may incorporate the proposed technique into an image system that can be operated to monitor multi-diseased tomato plants in fields

Beyond NDVI: Extraction of biophysical variables from remote sensing imagery

This chapter provides an overview of methods used for the extraction of biophysical vegetation variables from remote sensing imagery. It starts with the description of the main spectral regions in the optical window of the electromagnetic spectrum based on typical spectral signatures of land surfaces. Subsequently, the merit and problems of using radiative transfer models to describe the relationship between spectral measurements and biophysical and chemical variables of vegetation are described. Next, the use of statistical methods by means of vegetation indices for the same purpose gets attention. An overview of different types of indices is given without having the ambition in being exhaustive. Subsequently, an overview is provided of the biogeophysical vegetation variables that can directly be estimated from optical remote sensing observations, with emphasis on using vegetation indices. These vegetation variables are: (1) chlorophyll and nitrogen, (2) vegetation cover fraction and fAPAR, (3) leaf area index, and (4) canopy water. Finally, an outlook for a major research direction in the near future in this context is provided