Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane ( Saccharum officin...Published: 01 December 2015 by Elsevier BV in Renewable and Sustainable Energy Reviews
Greenhouse gas mitigation potential from green harvested sugarcane scenarios in São Paulo State, BrazilPublished: 01 December 2013 by Elsevier BV in Biomass and Bioenergy
Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, BrazilPublished: 01 July 2013 by Institute of Electrical and Electronics Engineers (IEEE) in IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium
After the launching of the Landsat-5/TM in the 1980's, remote sensing based methods appeared as an alternative to obtain agricultural statistics. Using the case study of sugarcane acreage in São Paulo State, this paper compares, at a municipal scale, the differences between the remote sensing based (Canasat Project) and the official (Brazilian Institute of Geography and Statistics - IBGE) estimates of relative sugarcane acreage in 2010. This evaluation was carried out through graphical and spatial statistical analyses. Despite individual divergences, results indicated that on average Canasat and IBGE are in agreement. Moreover, large differences were rarely observed for municipalities with high sugarcane production. The remote sensing based method seems to be of great potential for agricultural statistics as it tends to maintain the consistence of current official agricultural statistics with the benefit of further improving estimates. The spatial statistical analyses showed to be effective to assess remote sensing based acreage estimates.
A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central BrazilPublished: 19 October 2012 by MDPI in Remote Sensing
The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of −0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset.
Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large...Published: 01 August 2012 by Elsevier BV in Remote Sensing of Environment
Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in BrazilPublished: 02 April 2012 by MDPI in Sustainability
The use of biofuels to mitigate global carbon emissions is highly dependent on direct and indirect land use changes (LUC). The direct LUC (dLUC) can be accurately evaluated using remote sensing images. In this work we evaluated the dLUC of about 4 million hectares of sugarcane expanded from 2005 to 2010 in the South-central region of Brazil. This region has a favorable climate for rain-fed sugarcane, a great potential for agriculture expansion without deforestation, and is currently responsible for almost 90% of Brazilian’s sugarcane production. An available thematic map of sugarcane along with MODIS and Landast images, acquired from 2000 to 2009, were used to evaluate the land use prior to the conversion to sugarcane. A systematic sampling procedure was adopted and the land use identification prior to sugarcane, for each sample, was performed using a web tool developed to visualize both the MODIS time series and the multitemporal Landsat images. Considering 2000 as reference year, it was observed that sugarcane expanded: 69.7% on pasture land; 25.0% on annual crops; 0.6% on forest; while 3.4% was sugarcane land under crop rotation. The results clearly show that the dLUC of recent sugarcane expansion has occurred on more than 99% of either pasture or agriculture land.
Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, BrazilPublished: 13 December 2011 by MDPI in Remote Sensing
Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of São Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice to 2014, a “Green Ethanol” Protocol was established in 2007. The present work aims at analyzing five years of continuous sugarcane harvest monitoring, based on remote sensing images, to evaluate the effectiveness of the Protocol, thus helping decision makers to establish public policies to meet the Protocol’s expected goals. During the last five crop years, sugarcane acreage expanded by 1.5 million ha, which was compensated by a correspondent increase in the green harvested land. However, no significant reduction was observed in the amount of pre-harvest burned land over the same period. Based on the current trend, this goal is likely to be achieved one or two years later (2015–2016), which will be five or six years ahead of 2021 as the goal in the State Law number 11241 states. We thus conclude that the“Green Ethanol” Protocol has been effective with a positive impact on the increase of GH, especially on recently expanded sugarcane fields.
Mapeamento das áreas de cana-de-açúcar na região norte fluminense - RJ por uso de técnicas de sensoriamento remotoPublished: 01 June 2011 by FapUNIFESP (SciELO) in Engenharia Agrícola
This work aimed to develop an R algorithm for land use classification based on the relationships among the land use and variables associated to its occurrence on remote sensing images. The algorithm was tested for soybean crop identification in the Brazilian Soy Moratorium context. Probability functions were modeled based on the number of pixels within discrete intervals. The result was encouraging with overall classification accuracy greater than 80%, indicating that the method is promising also to be applied for other land use classifications. The R algorithm is available at http://www.dsr.inpe.br/∼mello.
Land use conversion is a key factor in the mitigation of GHG emission. Maximum mitigation can be achieved when degraded pasture land is converted to biofuel crops. Remote sensing images, and in particular the MODIS time series data, have a great potential to asses degraded pasture land. This work has the objective to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MODIS time series were used to obtain vegetation indices and fraction images. The wavelet technique was applied at various levels of decomposition to extract the input parameters in the WEKA J48 classifier. Pasture land was well distinguished from Cerrado. The distinction among different pasture land presented lower performance with best results for pasture with invasive plants followed by good pasture. Pasture land with bare soil patches and termite mounds were not distinguished from other classes of pasture.