Banca de DEFESA: ROGERIO DE SOUZA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : ROGERIO DE SOUZA SILVA
DATA : 27/02/2023
HORA: 08:00
LOCAL: Meet On Line
TÍTULO:

RAINFALL VARIABILITY, CALCULATION OF THE STANDARDIZED RAINFALL INDEX AND AREA MAPPING APPLIED TO COFFEE IN THE NORTHWEST OF MATO GROSSO STATE

PALAVRAS-CHAVES:

Rainfall, SPI, Conilon, remote sensing, OLI sensor.

PÁGINAS: 102
GRANDE ÁREA: Ciências Agrárias
ÁREA: Engenharia Agrícola
RESUMO:

The commercial Conilon coffee plantations in Mato Grosso are undergoing transformations with the adoption of new technologies. Regardless of the technological level, the regional climate determines the success of the crop, making or not the maximum agricultural efficiency possible. The northwest region of Mato Grosso, composed of seven municipalities, has the largest amount of cultivated areas with coffee when compared to the other internal regions of the state, and the municipality of Colniza has the largest concentration of plantations in the state. It is essential to know the spatial distribution of coffee activity to measure and plan its growth, as well as to efficiently structure the storage and commercialization of the production. In the first chapter, the study aimed to evaluate the annual and monthly rainfall variability, define the probability of monthly precipitation, employing mean, standard deviation and inverse gamma distribution functions from Excel version 365. Also for the climate analysis, the monthly and annual SPI drought index was calculated via the SPEI package in the RStudio program. To realization of the study the daily rainfall records were surveyed, adding up the totals for each month of the historical series for each of the four rainfall stations of the National Agency for Water and Basic Sanitation - ANA - representing the Northwestern, Aripuanã Station (36 years), Colniza Station (21 years), Cotriguaçu Station (17 years) and Juína Station (36 years). At the end, it was possible to relate the estimated data in relation to the water requirement for coffee production. The second chapter proposed to develop an image processing technique, using scenes generated from the OLI / Landsat-8 sensor, applying the GEOBIA - GEographic Object-Based Image Analysis method, through JavaScript language implemented in the Google Earth Engine platform, to quantify the spatial distribution of coffee cultivation, using the Random Forest classifier, for the municipality of Colniza, the largest coffee producer in the state. For the processing were made the calculations of the vegetation indices NDVI - Normalized Differential Vegetation Index, EVI - Enhanced Vegetation Index and PVI - Perpendicular Vegetation Index, calculation GLCM - Grey Level Co-Ocurrense Matrix for characterization of the objects present in the image, in addition to 729 samples geolocated between coffee and non-coffee for classification and validation. The study observed that the annual and monthly rainfall distribution attends the minimum hydric needs for Conilon coffee in most of the year, with more than 50% probability of occurring rainfall close to the historical monthly average in the wettest months. The monthly SPI-1 index at the stations revealed a total of 56 very dry and 34 extremely dry months. Then, the annual SPI-12 index pointed to seven very dry and five extremely dry years. The mapping revealed a spatialization of 10,217.62 hectares, a quantity superior to the data informed by Conab - Companhia Nacional de Abastecimento (National Company of Food Supply) and IBGE - Instituto Brasileiro de Geografia e Estatística (Brazilian Institute of Geography and Statistics), pointing to a concordance of 88% of Kappa and 94% for overall accuracy.

MEMBROS DA BANCA:
Presidente - 131916001 - RIVANILDO DALLACORT
Interno - 265126001 - CARLOS ANTONIO DA SILVA JUNIOR
Externo à Instituição - MARCELO SACARDI BIUDES - UFMT