ABSTRACT Desertification is the degradation process caused by climatic conditions and human activities that results in loss of soil productivity and decline in vegetation growth in the long term. Several indices related to vegetation, soil and climate are used to monitor desertification, but few studies explore qualitative and quantitative aspects of indices on desertification on spatial and hierarchical scale. This study aims to identify and measure indices related to increased risk of desertification on global, local and hierarchical scales using multinomial logistic regression models. Images from TM, ETM+ and OLI sensor from 1997 to 2018 in the end of dry and rainy seasons were used to quantify NDVI, TGSI, albedo, temperature, aridity index, evapotranspiration and precipitation on global spatial scale (Irauçuba Centro Norte) and local spatial scale (Miraíma, Canindé, Irauçuba and Santa Quitéria). GISD was calculated by geometric mean of weighted indices and segmented into 8 classes of susceptibility to desertification (hierarchical scale). The results showed that the best models were obtained on local scale and for the end of the rainy season. Temperature proved to be the most important variable for increased risk of desertification on global, local and hierarchical scales. Therefore, the increase in the risk of desertification in the studied areas is due to human activities of deforestation, overgrazing and fire. These factors contributed to reduction of vegetation cover and increase in temperature, changing the microclimatic, which led to decline in precipitation and worsening of desertification. term TM ETM 199 201 NDVI TGSI albedo temperature index Norte Miraíma, Miraíma (Miraíma Canindé Quitéria. Quitéria . Quitéria) scale) season Therefore deforestation fire microclimatic 19 20 1 2

ABSTRACT Careful implementation of a filtration system is essential for maintaining the operation of an irrigation system. Failure to maintain a filtration system can have a negative eff ect on irrigation pressure and uniformity. To avoid this problem, it is important to clean the filters, which can be done either manually or automatically. Predicting the correct time to clean the filters helps maintain the pressure and efficiency of the system. The aim of this study was to model backwash pressure as a function of water quality and the filter inlet pressure load using artificial neural networks. The characteristics of the water were determined using sensors to measure the pH (hydrogen potential), turbidity, total dissolved solids (TDS), and temperature. A pressure transducer was used to quantify the drop in pressure and the need to clean the filters. To predict the need for cleaning the irrigation filters, a hydraulic structure was constructed that included a screen filtration system with a mesh size of 120, cleaned by backwashing. The need for cleaning estimated by the multilayer perceptron feedforward artificial neural networks with 2-4-1 architecture performed well in modelling the temporal evolution of the pressure load in the screen filtration system (120 mesh), whereas adjusting the pressure load based on the water quality characteristics (pH, turbidity, total dissolved solids and temperature) performed poorly. uniformity problem automatically hydrogen potential, potential , potential) turbidity TDS, TDS (TDS) temperature 120 backwashing 241 2 4 1 2-4- (12 mesh, mesh) pH, (pH poorly (TDS 12 24 2-4 (1 2- (

Abstract Plant community succession is generally approached with phytosociological methods, but field surveys are time-consuming, expensive, and limited to several of sites. Remote sensing offers an efficient and economical way to analyze vegetation on large extensions and in inaccessible areas. Most studies addressing remote sensing and tree community succession refer to forest physiognomies. We investigated whether structural changes that occur in non-forest physiognomies are identified by multispectral sensor images (OLI-Landsat). Thirteen 0.1-ha plots were set up in Caatinga fragments aging 10-15, 20-25, 30-35, 40-45 and >50 years to calculate the total density of individuals (TD), mean canopy height (H), total basal area (G) and total aboveground biomass (AGB). We performed correlation analyses between these structural descriptors and eight remote sensing variables (reflectance data and spectral indices) obtained from Landsat images at the end of the rainy season and during the dry season. Blue and short-wave infrared reflectances were negatively correlated with mean height, basal area and biomass, regardless of the analyzed scene (coefficients between -0.58 and -0.79). The litter layer (a non-photosynthetic vegetation component) and the soil exposure are important factors influencing the spectral data.