Adélia Sousa

Evaluating the performance of multitemporal image compositing algorithms for burned area analysis

The main objective of this study was to compare the adequacy of various multitemporal image compositing algorithms to produce composite images suitable for burned area analysis. Satellite imagery from the NOAA Advanced Very High Resolution Radiometer (AVHRR) from three different regions (Portugal, central Africa, and South America) were used to compare six algorithms, two of which involve the sequential application of two criteria. Performance of the algorithms was assessed with the Jeffries-Matusita distance, to quantify spectral separability of the burned and unburned classes in the composite images. The ability of the algorithms to avoid the retention of cloud shadows was assessed visually with red-green-blue colour composites, and the level of radiometric speckle in the composite images was quantified with the Morans I spatial autocorrelation statistic. The commonly used NDVI maximum value compositing procedure was found to be the least appropriate to produce composites to be used for burned area mapping, from all standpoints. The best spectral separability is provided by the minimum channel 2 (m2) compositing approach which has, however, the drawback of retaining cloud shadows. A two-criterion approach which complements m2 with maximization of brightness temperature in a subset of the data (m2M4) is considered the better method.

Functions for aboveground biomass estimation derived from satellite images data in Mediterranean agroforestry systems

Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. The main goal of this study is the development of functions for the estimation of aboveground biomass, using crown cover as independent variable, for Quercus rotundifolia, Quercus suber and Pinus pinea in agroforestry systems, both for monospecies and multispecies stands, based on Portuguese data. Crown cover per specie was derived from crown horizontal projection obtained by processing very high spatial resolution satellite images (Quickbird and Worldview-2), with contrast split segmentation method and object-oriented classification. The stand species composition distinguished species and monospecies from multispecies stands. The best model was the one with crown cover and dummy variables for composition as explanatory variables, reflecting the differences between species and stand structure. Aboveground biomass with this function should ideally be calculated with the grid areas applied in this study, though similar accuracies can be obtained for other grid sizes.

Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment.

Differential vineyard fertilizer management based on nutrient,s spatio-temporal variability

Conventionally, vineyard fertilizer management has been based on information from composite soil samples and no account has been taken of the existing spatial variability in soil fertility. This study presents a quantitative analysis of soil phosphorus (P2O5) and potassium (K2O) content as well as pH carried out in an 80 ha vineyard, during 2011 and 2013 in order to identify their spatial variability and temporal stability. Additionally a quantitative analysis of plant P2O5 and K2O content was carried out in 2013 with the objective of evaluating the spatial variability of plant nutrients.In 2013 a contact sensor was used to survey soil apparent electrical conductivity (ECa) and an active optical sensor was used to measure the plant Normalized Difference Vegetation Index (NDVI). The results showed a low potential for implementing site-specific management of phosphorus fertilizer but an interesting potential for implementing site-specific management of potassium fertilizer and pH correction. The concentration of P2O5 and K2O in the plant showed a CV<30%, with adequate values in almost the entire area of the field, in contrast to the concentration of these main macronutrients in the topsoil. These results show that for differential nutrient management of vineyards, plant nutrient concentration is a more stable tool than soil nutrients concentration. The ECa and the NDVI presented weak correlations with soil and plant concentration of, , respectively, P2O5 and K2O, which shows that further development of vegetation operational sensors is needed to support decision making in the vineyard fertilization management.