Avoiding burning practice and its consequences on the greenhouse gas emission in sugarcane areas southern Brazil.

There is a growing need of sustainable solutions for balancing agricultural production with the reduction of its environmental impacts. The rapid increase in sugarcane cultivation and the progressive conversion of pre-harvest burning (BH) to green harvest (GH) have brought into debate the contribution of agricultural sector to the greenhouse gas (GHG) mitigation. This study focused on the estimated GHG emission from sugarcane cultivation during years in which sugarcane areas in southern Brazil expanded and passed throughout an important transition, from 2006 to 2012, when harvest adopted was changed from burned to not-burned based. Sugarcane management and harvest were mapped through visual interpretation of Landsat-type satellite images, and the areas under sugarcane cultivation were distinguished according to each agricultural phase and harvest regime (i.e., manual harvest with burning vs. green mechanized harvest). Based on a broad data review and applying the IPCC (2006) methodologies, the results were expressed in terms of kilograms of carbon dioxide equivalent (kg CO2eq ha-1). Avoiding burn prior to harvest, even during expansion of sugarcane areas, promoted a mean reduction of GHG emission from 901 to 686 kg CO2eq ha-1 relative to harvest phase (24% lower) and an increase from 1418.3 to 1507.9 kg CO2eq ha-1 related to the ratoon maintenance phase (6% higher). Analyzing the total GHG emission per unit of cultivated sugarcane area (hectare), it was observed a decrease from 2275 to 2034 kg CO2eq ha-1 (11% reduction). The gradual transition of pre-harvest burning on that period has contributed to the reduction of GHG emission associated with sugarcane production being an important step towards GHG mitigation while still providing more sustainable sugar and ethanol production in southern Brazil.

Mapping and Monitoring Zero-Deforestation Commitments.

A growing number of companies have announced zero-deforestation commitments (ZDCs) to eliminate commodities produced at the expense of forests from their supply chains. Translating these aspirational goals into forest conservation requires forest mapping and monitoring (M&M) systems that are technically adequate and therefore credible, salient so that they address the needs of decision makers, legitimate in that they are fair and unbiased, and scalable over space and time. We identify 12 attributes of M&M that contribute to these goals and assess how two prominent ZDC programs, the Amazon Soy Moratorium and the High Carbon Stock Approach, integrate these attributes into their M&M systems. These programs prioritize different attributes, highlighting fundamental trade-offs in M&M design. Rather than prescribe a one-size-fits-all solution, we provide policymakers and practitioners with guidance on the design of ZDC M&M systems that fit their specific use case and that may contribute to more effective implementation of ZDCs.

Air Quality and Health Impacts of Future Ethanol Production and Use in São Paulo State, Brazil.

It is often argued that liquid biofuels are cleaner than fossil fuels, and therefore better for human health, however, the evidence on this issue is still unclear. Brazil's high uptake of ethanol and role as a major producer makes it the most appropriate case study to assess the merits of different biofuel policies. Accordingly, we modeled the impact on air quality and health of two future fuel scenarios in São Paulo State: a business-as-usual scenario where ethanol production and use proceeds according to government predictions and a counterfactual scenario where ethanol is frozen at 2010 levels and future transport fuel demand is met with gasoline. The population-weighted exposure to fine particulate matter (PM2.5) and ozone was 3.0 µg/m³ and 0.3 ppb lower, respectively, in 2020 in the scenario emphasizing gasoline compared with the business-as-usual (ethanol) scenario. The lower exposure to both pollutants in the gasoline scenario would result in the population living 1100 additional life-years in the first year, and if sustained, would increase to 40,000 life-years in year 20 and continue to rise. Without additional measures to limit emissions, increasing the use of ethanol in Brazil could lead to higher air pollution-related population health burdens when compared to policy that prioritizes gasoline.

MAPSAR Image Simulation Based on L-band Polarimetric Data from the SAR-R99B Airborne Sensor (SIVAM System).

This paper describes the methodology applied to generate simulated multipolarized L-band SAR images of the MAPSAR (Multi-Application Purpose SAR) satellite from the airborne SAR R99B sensor (SIVAM System). MAPSAR is a feasibility study conducted by INPE (National Institute for Space Research) and DLR (German Aerospace Center) targeting a satellite L-band SAR innovative mission for assessment, management and monitoring of natural resources. Examples of simulated products and their applications are briefly discussed.

Superfícies de resposta espectro-temporal de imagens do sensor MODIS para classificação de área de soja no Estado do Rio Grande do Sul / Spectral-temporal response surface of MODIS sensor images for soybean area classification in Rio Grande do Sul State

Este trabalho objetivou avaliar o potencial e as limitações das imagens MODIS para classificação e estimativa de área de soja por meio do método de superfície de resposta espectro-temporal (Spectral-Temporal Response Surface - STRS). Um mapa temático das áreas com soja, oriundo da classificação de imagens Landsat do Estado do Rio Grande do Sul, foi utilizado como referência para auxiliar na orientação da amostragem dos pixels de treinamento e para a comparação dos resultados. Seis imagens compostas do sensor MODIS foram utilizadas para a classificação supervisionada da área de soja por meio do algoritmo de máxima verossimilhança (MAXVER) adaptado ao método STRS. Os resultados foram avaliados pelo coeficiente Kappa para a totalidade da área em estudo e também para uma região de latifúndios e outra de minifúndios. O método STRS subestimou em 6,6 por cento a área de soja para toda a região estudada, sendo que a estatística Kappa foi de 0,503. Para as regiões de latifúndios e minifúndios, a área de soja foi superestimada em 8 por cento (Kappa=0,424) e subestimada em 43,4 por cento (Kappa=0,358), respectivamente. As imagens MODIS, por meio do método STRS, demonstraram ter potencial para classificar a área de soja, principalmente em regiões de latifúndios. Em regiões de minifúndios, a correta identificação e classificação das áreas de soja mostrou-se pouco eficiente em razão da baixa resolução espacial das imagens MODIS.

This paper was aimed at evaluating the potential and the limitations of MODIS images for soybean classification and area estimation through a Spectral-Temporal Response Surface (STRS) method. A soybean thematic map from Rio Grande do Sul State, Brazil, derived from Landsat images was used as reference data to assist both sample training and results comparison. Six 16-day composite MODIS images were classified through a supervised maximum likelihood algorithm (MAXVER) adapted to the STRS method. The results were evaluated using the Kappa coefficient for the entire study area and for one region dominated by large farms and another by small ones. The STRS method underestimated the soybean area by 6.6 percent, for the entire study area, with a Kappa coefficient of 0.503. For regions with large and small farms the soybean area was overestimated by 8 percent (Kappa=0.424) and underestimated by 43.4 percent (Kappa=0.358), respectively. Eventually, MODIS images, through the STRS method, demonstrated good potential to classify and estimate soybean area, mainly in regions with large farms. For regions with small farms the correct identification and classification of soybean areas showed to be less efficient due to the low spatial resolution of MODIS images.

Estimativa da área de soja no Estado do Rio Grande do Sul por um método de amostragem / Soybean crop area estimate in Rio Grande do Sul State through a sampling method

Este trabalho objetivou avaliar um método de amostragem por segmentos regulares na estimativa da área plantada com soja no Estado do Rio Grande do Sul. Um mapa temático das áreas com soja, oriundo da classificacão multitemporal de imagens do satélite Landsat, ano-safra 2000/01, foi utilizado como dado de referência para comparacão dos resultados. A área de estudo foi dividida em segmentos regulares de 1 x 1km e estratificada em relacão ao percentual de soja cultivado no município, em três extratos: a) 0-20; b) 20-40 e c) 40-67 por cento. Um método probabilístico foi utilizado para definir quatro números amostrais, representando 0,06, 0,12, 0,24 e 0,48 por cento da área de estudo, sendo cada um sorteado aleatoriamente cem vezes. A estimativa da área de soja para cada sorteio foi calculada analisando-se a área de cada segmento sorteado sobre o mapa temático e então comparada ao dado de referência. Os melhores resultados foram obtidos para o maior número amostral, o qual teve baixo Coeficiente de Variacão (5,2 por cento), indicando que o método, além de fornecer a área plantada com soja, em nível estadual, pode ser usado para prever a área plantada no início da safra ou nos anos em que não se dispõe de imagens de satélite livres de nuvens. Os três melhores sorteios para o maior número amostral tiveram sua área de soja também quantificada através do mapeamento de imagens adquiridas no ano-safra subseqüente (2001/02). Neste caso, foi observado um incremento entre 11,4 e 12,5 por cento em relacão ao ano-safra 2000/01, indicando que o incremento informado pelo IBGE (8,8 por cento) está subestimado.