Nikolaos G. Silleos

Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years

Abstract During the last 30 years Vegetation Indices (VI) have been extensively used for tracing and monitoring vegetation conditions, such as health, growth levels, production, water and nutrients stress, etc. In this paper the characteristics of over 20 VIs based on the VNIR spectrum are described in order to provide the reader with adequate material to form a picture of their nature and purpose. It is not, though, a review article due to the fact that a huge volume of work exists all over the world and a simple lining up of the related papers would not contribute to an understanding of the usefulness of VIs. A limited number of review work is included, together with research results from various operational and research applications of VI for wheat damage assessment in Northern Greece.

Assessment of crop damage using space remote sensing and GIS

This paper reports on a new Earth Observation (EO) research field concerning the potential use of space remote sensing for the assessment of crop damage at the field level. Digital field (cadastral) maps were used in order to overcome the problem of poor field boundary distinction (due to the current spatial resolution, small field size, a hilly landscape and the homogeneity of the area cover) and to estimate damage at field level. The relationship between crop damage estimations made by field observations and Normalized Difference Vegetation Index (NDVI) data was studied. By transforming the cadastral (field) map into a GRID format containing cells of one metre square (ArcView, Spatial Analyst), it was possible to determine the number of cells overlaying pixels or part of pixels only within the field area and the corresponding mean NDVI value. Various techniques, including Supervised Classification and Regression Models, were applied in order to study the correlation between NDVI values and those estimated by Hellenic Organization of Agricultural Insurance (HOAI) experts. The results appear to be very promising and the HOAI has decided to continue the research using the next generation high-resolution satellites.

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: under‐sampling may not accurately describe the phenomenon under consideration, whilst over‐sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time‐series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi‐temporal NDVI composites following the proposed optimum temporal resolution. A time‐series of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi‐temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.

Rapid error assessment for quantitative estimations from Landsat 7 gap-filled images

The failure of the Scan Line Corrector (SLC) of the Landsat ETM+ (Enhanced Thematic Mapper Plus) instrument in 2003 had resulted in missing values for 22% of each scene. As the remaining pixels were of high quality, several procedures had been developed to fill the gaps and increase the usability of the SLC-off images. In this letter, a methodology is presented to assess the error when estimating quantitative parameters from gap-filled Landsat 7 images. The error from the gap-filling procedure was estimated using an external reference image. The methodology was applied in a Mediterranean river basin using two types of gap-filling methods and the error was estimated for leaf area index (LAI), actual evapotranspiration (ETa) and soil moisture in the rootzone (SMrz), three remotely sensed products which are commonly used in hydrological studies. The results suggest that the interpolation method had lower errors in all examined products. The proposed methodology is an imperative step that each user of gap-filled products could use to estimate the associated error before using the maps.

Relationships between remote sensing spectral indices and crops discrimination

Abstract Multitemporal and multispectral SPOT data were used for calculation of three spectral indices, (1) Radiometric means, (2) Vegetation Index (NDVI), (3) Brightness Index. The sequence reflectance, absorption, reflectance in bands 1,2 and 3 respectively, is common for all the studied crops and months (June and July). The highest differences in reflectance values are observed in July and especially in band 3 which proved very sensitive to chlorophyll development. Pearson correlation coefficients show that combination of band I and 2 or band I and 3 supply more thematic information than band 1 and 2. Discriminant Analysis shows that for sugarebeets, radiometric values and brightness index(BI) present the same classification accuracy. On the other hand Vegetation Index (NDVI) is sufficient for cotton and harvested alfalfa, while the classification accuracy of the other crops ranges between the Radiometric Values and Brightness Index. Pairwise squared generalized distances show that radiometric values a...

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.

Modeling LAI based on land cover map and NDVI using SPOT and Landsat data in two Mediterranean sites: preliminary results

Leaf Area Index (LAI) is considered to be a key parameter of ecosystem processes and it is widely used as input to biogeochemical process models that predict net primary production (NPP) or can be a useful parameter for crop yield prediction and crop stress assessment as well as estimation of the exchanges of carbon dioxide, water, and nutrients in forests. LAI can be derived from satellite optical data using models referred to physical-based approaches, which describe the physical processes of energy flow in the soil-vegetation-atmosphere system, and models using empirically derived regression relationships based on spectral vegetation indices (VIs). The first category of models are more general in application because they can account for the different sources of variability, although in many cases the information needed to constrain model inputs is not available. In contrast, empirical models depend on the site and time. The aim of this paper is to create a reliable semi-empirical method, applied in two Mediterranean sites, to estimate LAI with high spatial resolution images. The model uses a minimum dataset of a Landsat 5 TM or SPOT 4 XS image, land cover map and DEM for each area. Specifically, this model calculates the reflectance of initial bands implementing topographic correction with the aid of DEM and metadata of the images and afterwards uses a list of NDVI values that correspond to certain LAI values on different land cover types which has been proposed by the MODIS Land Team. This model has been applied in two areas; in the river basin of Nestos (Greece and Bulgaria) and in the river basin of Tamega (Portugal). The predicted LAI map was validated with ground truth data from hemispherical images showing high correlation, with r reaching 0.79 and RMSE less than 1 m2/m2.

Qualitative and Spatial Comparative Study of Satellite Images Classified by Supervised and Fuzzy Logic Based Classification Algorithms: A case study in Kilkis prefecture, Central Macedonia, Greece

Abstract In a first stage a SPOT image of the survey area was classified for land use/land cover classification, using the Maximum Likelihood Algorithm (MLC). However, due to the spatial uncertainty which exist mainly between the borders of the spectral categories, as they defined by MLC, in a second stage a supervised classification based on fuzzy classifiers was applied. A sigmoid function defines the degree that every pixel belongs in each category and differentiates the results of the classification in comparison with those of the classical Boolean logic. The results of the fuzzy classification leads to the construction of another land use/land cover map. For reasons of comparison between the two methods, the results of each classified category in both methods was converted to an integer binary image. As qualitative index of agreement between the two methods, the Kappa index of agreement and for each category was used. The results are evaluated with field work.