Remote sensing plays a significant role in local, regional and global monitoring of land covers. Ecological concerns worldwide determine the importance of remote sensing applications for the assessment of soil conditions, vegetation health and identification of stress-induced changes. The extensive industrial growth and intensive agricultural land-use arise the serious ecological problem of environmental pollution associated with the increasing anthropogenic pressure on the environment. Soil contamination is a reason for degradation processes and temporary or permanent decrease of the productive capacity of land. Heavy metals are among the most dangerous pollutants because of their toxicity, persistent nature, easy up-take by plants and long biological half-life. This paper takes as its focus the study of crop species spectral response to Cd pollution. Ground-based experiments were performed, using alfalfa, spring barley and pea grown in Cd contaminated soils and in different hydroponic systems under varying concentrations of the heavy metal. Cd toxicity manifested itself by inhibition of plant growth and synthesis of photosynthetic pigments. Multispectral reflectance, absorbance and transmittance, as well as red and far red fluorescence were measured and examined for their suitability to detect differences in plant condition. Statistical analysis was performed and empirical relationships were established between Cd concentration, plant growth variables and spectral response Various spectral properties proved to be indicators of plant performance and quantitative estimators of the degree of the Cd-induced stress.
Terminology is a key issue for a better understanding among people using various languages. Terminology accuracy is
essential during all phases of international cooperation. It is crucial to keep up with the latest quantitative and qualitative
developments and novelties of the terminology in advanced technology fields such as aerospace science and industry.
This is especially true in remote sensing and geoinformatics which develop rapidly and have wide and ever extending
applications in various domains of human activity. The importance of the correct use of remote sensing terms refers not
only to people working in this field but also to experts in many disciplines who handle remote sensing data and
information products. The paper is devoted to terminology issues that refer to all aspects of remote sensing research and
application areas. The attention is drawn on the recent needs and peculiarities of compiling specialized dictionaries in
the subject area of remote sensing. Details are presented about the work in progress on the preparation of an English-Bulgarian dictionary of remote sensing terms focusing on Earth observations and geoinformation science. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. Any interest in cooperation and initiating of suchlike collaborative multilingual projects is welcome and highly appreciated.
Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth
observations. It supplies valuable information on crop condition and growth processes. Much research has been carried
out on vegetation phenology issues. In agriculture, the timing of seasonal cycles of crop activity is important for species
classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of
remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the
seasonal spectral behaviuor of different species and their relation to crop vigour. For this reason, we performed groundbased
study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to
quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in
main ontogenetic periods. Phenology and agr-specific relationships allow to assess crop condition during different
portions of the growth cycle and thus effectively track plant development and make yield predictions. The applicability
of different vegetation indices for monitoring crop seasonal dynamics, health condition, and yield potential was
Recent developments in environmental studies are greatly related to worldwide ecological problems associated with
anthropogenic impacts on the biosphere and first of all on vegetation. Modern remote sensing technologies are involved in numerous ecology-related investigations dealing with problems of global importance, such as ecosystems preservation and biodiversity conservation. Agricultural lands are subjected to enormous pressure and their monitoring and assessment have become an important ecological issue. In agriculture, remote sensing is widely used for assessing plant growth, health condition, and detection of stress situations. Heavy metals constitute a group of environmentally hazardous substances whose deposition in soils and uptake by species affect soil fertility, plant development and productivity. This paper is devoted to the study of the impact of heavy metal contamination on the performance of agricultural species. The ability of different spectral indicators to detect heavy metal-induced stress in plants is examined and illustrated. Empirical relationships have been established between the pollutant concentration and plant growth variables and spectral response. This allows not only detection but quantification of the stress impact on plant performance.
At a time of rising global concern about environmental issues remote sensing techniques acquire increasing importance in vegetation state assessment and health diagnostics. Multispectral optical data have proved abilities in vegetation monitoring. The visible and near infrared region reveals significant sensitivity to plant biophysical variables and pigment content. The spectral signatures of leaves in this wavelength range are mostly defined by the composition of photosynthetic pigments and their stress-induced changes. As such, plant spectral response provides valuable information about the physiological status of plants. As far as chlorophyll content is a most important bioindicator of plant condition being responsible for light absorption and the photosynthetic process, techniques for its non-destructive assessment are of prime interest. In our study, multispectral data of reflected, transmitted and emitted by plants radiation have been used to reveal the performance of different spectral signatures in chlorophyll estimation. Vegetation indices, red edge shift, spectral transmittance, fluorescence parameters, and chromaticity features, have been related in a statistical manner to plant chlorophyll in order to examine the statistical significance of plant spectral response changes to chlorophyll variations. High correlations have been found permitting quantitative dependences to be established between chlorophyll in plants and their spectral properties. Empirical relationships have been derived that allow plant condition and stress assessment (in terms of chlorophyll inhibition) to be performed by using different spectral indicators.
Being recognized as a powerful tool in many scientific and application fields, remote sensing enters recently still wider
into its utilization stage when the goal is to bring the up-to-now investigation results to an operational use. Agricultural
monitoring is among the priorities of remote sensing observations supplying early information on the development and
growth conditions of crops. Various approaches have been used for crop behavior assessment in order to provide
objective, timely and quantitative yield forecasts at regional and national scales. Among these approaches are phenology
tracking, agro-meteorological modeling, remote sensing data implementation. On the other hand, continues the research
to improve the reliability of the results by implying, for instance, different sampling strategies, different statistical data
analysis and extrapolations, different data integration from various sources. In this paper we test an approach for yield
forecasting and verification of the predictions with consideration of plant phenology. It comprises the development of
simple yield prediction models based on key crop bioparameters; the development of crop spectral-biophysical
relationships for crop variables retrieval and yield prediction from multispectral reflectance data; verification of the
spectral predictions via crop yield agronomical indicators.
A significant amount of research has been performed to develop efficient methods for monitoring of vegetation
dynamics. A prevailing part of the work is devoted to multispectral data transformation techniques such as spectral bands
ratios and linear combinations (vegetation indices) as a mean for vegetation parameters estimation. Vegetation cover
fraction and chlorophyll assessment is a main objective in vegetation monitoring. In agriculture, for instance, vegetation
amount is related to plant growth monitoring, stress detection and yield forecasting. Here we use colorimetric analysis of
spectral reflectance data to examine the sensitivity of vegetation chromaticity features to chlorophyll and canopy fraction
changes. Two main factors influence vegetation visible and near infrared reflectance: plant senescence, i.e. chlorophyll
inhibition due to plant maturing or as a stress symptom, and soil spectral properties varying with soil type and surface
properties. The work was conducted in order to reveal plant senescence effects and soil background impact on vegetation
reflectance response and colorimetric characters behaviour.