Análise da dinâmica de alteração do coberto florestal na Reserva Florestal de Mecurubi - Moçambique

A desflorestação nos países em desenvolvimento contribui com 20 a 25% das emissões globais de dióxido de carbono. Em 2006 foi lançado o mecanismo para a Redução de Emissões por Desflorestação e Degradação Florestal, o qual preconiza o estabelecimento de parcerias entre países desenvolvidos e em desenvolvimento para a redução da desflorestação. O presente estudo pretende contribuir para a avaliação da elegibilidade da reserva florestal de Mecuburi como área de intervenção nesse mecanismo. Para cartografar as alterações do coberto/uso do solo e determinar as taxas histórica de desflorestação, classificaram-se imagens de satélite de três datas na década de 2000 (2002, 2007 e 2011). Posteriormente, através da aplicação do modelo GEOMOD, produziu-se uma previsão da localização da desflorestação para o ano 2020. O rigor estimado para a classificação das imagens de satélite foi superior a 95% para todas as datas, contudo, não foi possível realizar uma validação formal da classificação devido à falta de dados de campo. Na totalidade da reserva verificou-se um aumento da floresta, todavia, a área cartografada como floresta em 2002 sofreu uma redução significativa durante o período em análise a uma taxa bruta de desflorestação de 2165 ha/ano. A desflorestação projectada para 2020 incide na zona norte da reserva. Um projecto REDD na reserva contribuiria para reduzir a desflorestação; ABSTRACT: Deforestation in developing countries accounts for 20 to 25% of the global carbon emissions. Since 2006 a mechanism for reduction of emissions from deforestation and forest degradation is under discussion at the UNFCCC. The aim is to promote the partnership between developed and developing countries in order to reduce deforestation. This study intends to contribute to the assessing the eligibility of the Mecuburi forest reserve for REDD intervention. Remote sensing was used to map the land cover/use changes between three dates, 2002, 2007 and 2011. Gross and net deforestation rates were calculated and the location of deforestation in 2020 was projected using GEOMOD. The classification algorithm yielded an overall accuracy above 95% for the three images however, no field data was available to formally validate the classification. There was an overall increase of forest area during the analyzed time period. However, the benchmark forest area (2002) was reduced at a gross rate of 2165 ha/year. Most of the projected deforestation is located to the north of the reserve. A REDD project could contribute to reduce deforestation within the reserve

Retrieval of maize leaf area index using hyperspectral and multispectral data

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study areainfo:eu-repo/semantics/publishedVersio

Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes