Banca de DEFESA: IARA BEZERRA DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : IARA BEZERRA DA SILVA
DATE: 01/03/2024
TIME: 09:00
LOCAL: Laboratório de informática NESC/Remoto
TITLE:

ESTIMATE OF GROSS PRIMARY PRODUCTION IN A SEASONALLY DRY TROPICAL FOREST

KEY WORDS:

GPP (Gross Primary Productivity); Vegetation Index; Landsat 8; Google Earth Engine; Caatinga biome.

PAGES: 82
BIG AREA: Ciências Exatas e da Terra
AREA: Geociências
SUBÁREA: Meteorologia
SPECIALTY: Meteorologia Aplicada
SUMMARY:

Gross Primary Production (GPP) is characterized by the rate of carbon absorption during photosynthesis, providing crucial insights into seasonal variations in the carbon cycle. GPP has been monitored worldwide through flux towers, using the Eddy Covariance (EC) technique, and models combined with remote sensing data. This study aimed to evaluate different methods for estimating GPP from remote sensing, derived from Landsat 8 and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The following models were tested: Vegetation Photosynthesis Model (VPM), Temperature and Greenness Model (TG), Vegetation Index Model (VI), Light Use Efficiency (LUE), and MOD17A2H product, at the Seridó Ecological Station (ESEC-Seridó), in the municipality of Serra Negra do Norte - RN. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) were derived from Landsat 8, available on the Google Earth Engine (GEE), used as inputs in the models, and integrated with micrometeorological variables. The models were compared with GPP measured by the Eddy Covariance technique (GPPEC). Overall, all models underestimated GPPEC, especially in the dry season, except for the MODIS model, which overestimated in both seasons, particularly in the rainy season, by about 394%. The results indicated that the TG model estimated using Landsat 8 data showed the best performance for the study area (seasonally dry tropical forest). The second-best performance was from the LUE models, also estimated from Landsat 8, when considering variability in light use efficiency. Therefore, the validation and comparison of models conducted in this study will provide valuable insights for the development of future models for estimating gross primary production, given the need for model calibration using observed data to reduce uncertainties arising from the parameters used.

COMMITTEE MEMBERS:
Presidente - 1753398 - PABLO ELI SOARES DE OLIVEIRA
Interno - 2086472 - BERGSON GUEDES BEZERRA
Externo à Instituição - BERNARDO BARBOSA DA SILVA - UFCG