A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

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Using the satellite-derived NDVI to assess ecological responses to environmental change

This paper on reports the production of a 1km spatial resolution land cover classie cation using data for 1992- 1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar e A ort to create a product at 8km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37294 O 1km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multi- temporal AVHRR metrics were used to predict class memberships. Minimum annual red ree ectance, peak annual Normalized Di A erence Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the e nal product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ran- ging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1km pixels consisting of greater than 90% one class within the high-resolution data sets.

Global land cover classification at 1 km spatial resolution using a classification tree approach

Improvements to a MODIS global terrestrial evapotranspiration algorithm

Articles R emote sensing has facilitated extraordinary advances in the modeling, mapping, and understanding of ecosystems. Typical applications of remote sensing involve either images from passive optical systems, such as aerial photography and Landsat Thematic Mapper (Goward and Williams 1997), or to a lesser degree, active radar sensors such as RADARSAT (Waring et al. 1995). These types of sensors have proven to be satisfactory for many ecological applications , such as mapping land cover into broad classes and, in some biomes, estimating aboveground biomass and leaf area index (LAI). Moreover, they enable researchers to analyze the spatial pattern of these images. However, conventional sensors have significant limitations for ecological applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and leaf area index (Waring et al. 1995, Carlson and Ripley 1997, Turner et al. 1999). They are also limited in their ability to represent spatial patterns: They produce only two-dimensional (x and y) images, which cannot fully represent the three-dimensional structure of, for instance, an old-growth forest canopy.Yet ecologists have long understood that the presence of specific organisms, and the overall richness of wildlife communities, can be highly dependent on the three-dimensional spatial pattern of vegetation (MacArthur and MacArthur 1961), especially in systems where biomass accumulation is significant (Hansen and Rotella 2000). Individual bird species, in particular, are often associated with specific three-dimensional features in forests (Carey et al. 1991). In addition, other functional aspects of forests, such as productivity, may be related to forest canopy structure. Laser altimetry, or lidar (light detection and ranging), is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third (z) dimension. Lidar sensors directly measure the three-dimensional distribution of plant canopies as well as subcanopy topography, thus providing high-resolution topographic maps and highly accurate estimates of vegetation height, cover, and canopy structure. In addition , lidar has been shown to accurately estimate LAI and aboveground biomass even in those high-biomass ecosystems where passive optical and active radar sensors typically fail to do so. The basic measurement made by a lidar device is the distance between the sensor and a target surface, obtained by determining the elapsed time between the emission of a short-duration laser pulse and the arrival of the reflection of that pulse (the return signal) at the sensor's receiver. Multiplying this …

https://www.jstor.org/stable/10.1641/0006-3568(2002)052%5B0019:LRSFES%5D2.0.CO;2

Lidar Remote Sensing for Ecosystem Studies

A small field campaign was conducted during August 1997 in the Piano di Rosia area in Tuscany, Italy. The terms of both the radiation balance and the surface energy balance were measured by several techniques and for several field sites. Together with a LANDSAT-TM scene of 23 August 1997 of the same area, these data give an excellent opportunity for a profound study of interaction between the radiation and energy fluxes from point to regional scale. A new method to derive the surface energy fluxes from remote sensing measurements, called the Simplified Surface Energy Balance Index (S-SEBI), is developed, tested and validated with the available data. If the atmospheric conditions over the area can be considered constant and the area reflects sufficient variations in surface hydrological conditions the fluxes can be calculated without any further information than the remote sensing image itself. The results of this case study show that with the relative simple S-SEBI method the surface energy balance parameters can be estimated with a high precision. The measured and estimated evaporative fraction values have a maximum relative difference of 8%.

S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance

This letter describes the Harmonic ANalysis of Time Series (HANTS) algorithm. It performs two tasks: (i) screening and removal of cloud affected observations; and (ii) temporal interpolation of the remaining observations to reconstruct gapless images at a prescribed time. HANTS was applied to 36 AVHRR 10-days-maximum-NDVI composites covering most of Europe. The results show that cloud affected data are recognized successfully and replaced. Up to half the data points were rejected with no consequence for the successful reconstruction of seasonal NDVI profiles.

Reconstructing cloudfree NDVI composites using Fourier analysis of time series

A terrestrial laser scanner measures the distance to an object surface with a precision in the order of millimeters. The quality of the individual points in a point cloud, although directly affecting standard processing steps like point cloud registration and segmentation, is still not well understood. The quality of a scan point is influenced by four major factors: instrument mechanism, atmospheric conditions, object surface properties and scan geometry. In this paper, the influence of the scan geometry on the individual point precision or local measurement noise is considered. The local scan geometry depends on the distance and the orientation of the scanned surface, relative to the position of the scanner. The local scan geometry is parameterized by two main parameters, the range, i.e. the distance from the object to the scanner and the incidence angle, i.e. the angle between incoming laser beam and the local surface normal. In this paper, it is shown that by studying the influence of the local scan geometry on the signal to noise ratio, the dependence of the measurement noise on range and incidence angle can be successfully modeled if planar surfaces are observed. The implications of this model is demonstrated further by comparing two point clouds of a small room, obtained from two different scanner positions: a center position and a corner position. The influence of incidence angle on the noise level is quantified on scans of this room, and by moving the scanner by 2 m, it is reduced by 20%. The improvement of the standard deviation is significant, going from 3.23 to 2.55 mm. It is possible to optimize measurement setups in such a way that the measurement noise due to bad scanning geometry is minimized and therefore contribute to a more efficient acquisition of point clouds of better quality.

Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points

The distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Satellite data provide the opportunity to monitor continuously the dynamics of vegetation, its changes and its impact on the environment. Monthly normalized difference vegetation index (NDVI) values which had been extracted from NOAA-AVHRR GAC data for Southern Africa (south of the Equator) from 1981 till 1991 were analysed using the Fast Fourier Transform (FFT). The FFT gives a new representation of the time series of images, i.e. a set of images of amplitude and phase (pixelwise) of periodic functions with different frequencies, which allows the analysis of the vegetation phenology using only the amplitude and phase of the most important periodic components. This approach is a powerful way to monitor various dynamic parameters of the vegetation in Southern Africa. The relationship between amplitude values and aridity and vegetation type was studied by correlating the amplitude and...

Massimo Menenti

Papers

A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation

S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance

Reconstructing cloudfree NDVI composites using Fourier analysis of time series

Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points

Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data