Rise in the frequency of cloud cover in LANDSAT data for the period 1973 to 1981

Percentages of cloud cover in LANDSAT imagery were used to calculate the cloud cover monthly average statistic for each LANDSAT scene in Brazil, during the period of 1973 to 1981. The average monthly cloud cover and the monthly minimum cloud cover were also calculated for the regions of north, northeast, central west, southeast and south, separately

Método EPS para manejo da irrigação de forrageiras.

bitstream/CPPSE-2010/19159/1/PROCICircT63FCM2009.00422.pd

Estimation of the sugar cane cultivated area from LANDSAT images using the two phase sampling method

A two phase sampling method and the optimal sampling segment dimensions for the estimation of sugar cane cultivated area were developed. This technique employs visual interpretations of LANDSAT images and panchromatic aerial photographs considered as the ground truth. The estimates, as a mean value of 100 simulated samples, represent 99.3% of the true value with a CV of approximately 1%; the relative efficiency of the two phase design was 157% when compared with a one phase aerial photographs sample

The Relationship Between Lower Extremity Functional Strength and Aerobic Performance in Youth with Cerebral Palsy (CP)

Please refer to the pdf version of the abstract located adjacent to the title

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part I. Method development and toy model demonstration

Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g., impulse or step experiments, and if the system is noisy, these experiments need to be repeated several times to obtain good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation are sufficient if in addition data from an unperturbed (control) experiment are available. To identify the linear response function for this ill-posed problem, we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: this is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise-level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon cycle response functions in Part 2 of this study (Torres Mendonça et al., 2021a). However, the core of our method, namely our new approach to obtaining the noise level for a proper estimation of the regularization parameter, may find applications in also solving other types of linear ill-posed problems

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part II. Application to the land carbon cycle in the MPI Earth System Model

The response function identification method introduced in the first part of this study is applied here to investigate the land carbon cycle in the Max Planck Institute for Meteorology Earth System Model. We identify from standard C4MIP 1 % experiments the linear response functions that generalize the land carbon sensitivities β and γ. The identification of these generalized sensitivities is shown to be robust by demonstrating their predictive power when applied to experiments not used for their identification. The linear regime for which the generalized framework is valid is estimated, and approaches to improve the quality of the results are proposed. For the generalized γ sensitivity, the response is found to be linear for temperature perturbations until at least 6 K. When this sensitivity is identified from a 2×CO2 experiment instead of the 1 % experiment, its predictive power improves, indicating an enhancement in the quality of the identification. For the generalized β sensitivity, the linear regime is found to extend up to CO2 perturbations of 100 ppm. We find that nonlinearities in the β response arise mainly from the nonlinear relationship between net primary production and CO2. By taking as forcing the resulting net primary production instead of CO2, the response is approximately linear until CO2 perturbations of about 850 ppm. Taking net primary production as forcing also substantially improves the spectral resolution of the generalized β sensitivity. For the best recovery of this sensitivity, we find a spectrum of internal timescales with two peaks, at 4 and 100 years. Robustness of this result is demonstrated by two independent tests. We find that the two-peak spectrum can be explained by the different characteristic timescales of functionally different elements of the land carbon cycle. The peak at 4 years results from the collective response of carbon pools whose dynamics is governed by fast processes, namely pools representing living vegetation tissues (leaves, fine roots, sugars, and starches) and associated litter. The peak at 100 years results from the collective response of pools whose dynamics is determined by slow processes, namely the pools that represent the wood in stem and coarse roots, the associated litter, and the soil carbon (humus). Analysis of the response functions that characterize these two groups of pools shows that the pools with fast dynamics dominate the land carbon response only for times below 2 years. For times above 25 years the response is completely determined by the pools with slow dynamics. From 100 years onwards only the humus pool contributes to the land carbon respons

Using Near Infrared Spectroscopy to Access Muscle Post-exercise Oxygen Debt

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Dynamical trapping and relaxation of scalar gravitational fields

We present a framework for nonlinearly coupled scalar-tensor theory of gravity to address both inflation and core-collapse supernova problems. The unified approach is based on a novel dynamical trapping and relaxation of scalar gravity in highly energetic regimes. The new model provides a viable alternative mechanism of inflation free from various issues known to affect previous proposals. Furthermore, it could be related to observable violent astronomical events, specifically by releasing a significant amount of additional gravitational energy during core-collapse supernovae. A recent experiment at CERN relevant for testing this new model is briefly outlined.Comment: 4 pages; version to appear in PL

INPE's crop survey program using combined LANDSAT and aircraft data

There are no author-identified significant results in this report