Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

Rudorff, Bernardo Friedrich Theodor; Adami, Marcos; Risso, Joel; Aguiar, Daniel Alves; Pires, Bernardo; Amaral, Daniel Furlan; Fabiani, Leandro; Cecarelli, Izabel

doi:10.3390/su4051074

Cited by 30 publications

(27 citation statements)

References 44 publications

(53 reference statements)

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“…By contrast, OBCD methods are considered appropriate for images with high or very high spatial resolution (e.g., GeoEye, WorldView-I/II, QuickBird, IKONOS, and OrbView) [56,57]. The characteristics of the study area should also be considered when selecting a CD method [58][59][60]. A large area that uses low-and medium-resolution data frequently utilizes pixel-based techniques, whereas local scale studies that use very high-resolution data adopt object-based techniques.…”

Section: Lu/lc Change Detectionmentioning

confidence: 99%

Analysis of Water Resources in Horqin Sandy Land Using Multisource Data from 2003 to 2010

et al. 2016

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Over the past four decades, land use/land cover (LU/LC) change, coupled with persistent drought, has resulted in the decline of groundwater levels in Horqin Sandy Land. Accordingly, this study quantifies changes in LU/LC and groundwater storage (GWS). Furthermore, it investigates the effects of LU/LC changes on GWS. GWS changes are estimated using Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements obtained from July 2003 to December 2010. Soil moisture and snow water equivalent data derived from the Global Land Data Assimilation System (GLDAS) are used to isolate GWS changes from GRACE-derived terrestrial water storage changes. The result shows that the groundwater depletion rate in Horqin Sandy Land is 13.5˘1.9 mm¨year´1 in 2003-2010, which is consistent with the results of monitoring well stations. LU/LC changes are detected using bitemporal imageries (2003 and 2010) from Landsat Thematic Mapper through the post-classification comparison method. The result shows that LU/LC significantly changed during the aforementioned period. Bare soil and built-up land have increased by 76.6% and 82.2%, respectively, while cropland, vegetation, and water bodies have decreased by 14.1%, 74.5%, and 82.6%, respectively. The analysis of GWS and LU/LC changes shows that LU/LC changes and persistent drought are the main factors that affect groundwater resources.

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Section: Lu/lc Change Detectionmentioning

confidence: 99%

Analysis of Water Resources in Horqin Sandy Land Using Multisource Data from 2003 to 2010

et al. 2016

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“…The 'Soy Moratorium', as it became known, has been renewed annually since and has effectively halted the clearing of Amazon rainforests in Brazil for large-scale soy plantations. 4,5 The risk of failing to live up to environmental and forest conservation standards was clearly felt by paper giant Asia Pulp & Paper (APP) who after fierce public criticism of its role in converting large areas of Indonesian rainforests and peatlands to fast-growing timber plantations found itself losing dozens of major customers within the time span of a few years. As a result, in 2013 APP announced a new corporate policy, committing itself to stop the conversion of high carbon stock and high conservation value forests, working more closely and transparently with local communities affected by new plantations, and allowing independent audits of its policy by credible environmental organizations.…”

Section: Fig 1: Carbon Dioxide (Co 2 ) Emissions From Deforestation mentioning

confidence: 99%

Trading Forests: Quantifying the Contribution of Global Commodity Markets to Emissions from Tropical Deforestation

2014

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This paper aims to improve our understanding of how and where global supply-chains link consumers of agricultural and forest commodities across the world to forest destruction in tropical countries. A better understanding of these linkages can help inform and support the design of demand-side interventions to reduce tropical deforestation. To that end, we map the link between deforestation for four commodities (beef, soybeans, palm oil, and wood products) in eight case countries (Argentina, Bolivia, Brazil, Paraguay, Democratic Republic of the Congo, Indonesia, Malaysia, and Papua New Guinea) to consumption, through international trade. Although few, the studied countries comprise a large share of the internationally traded volumes of the analyzed commodities: 83% of beef and 99% of soybean exports from Latin America, 97% of global palm oil exports, and roughly half of (official) tropical wood products trade. The analysis covers the period 2000-2009. We find that roughly a third of tropical deforestation and associated carbon emissions (3.9 Mha and 1.7 GtCO2) in 2009 can be attributed to our four case commodities in our eight case countries. On average a third of analyzed deforestation was embodied in agricultural exports, mainly to the EU and China. However, in all countries but Bolivia and Brazil, export markets are dominant drivers of forest clearing for our case commodities. If one excludes Brazilian beef on average 57% of deforestation attributed to our case commodities was embodied in exports. The share of emissions that was embodied in exported commodities increased between 2000 and 2009 for every country in our study except Bolivia and Malaysia.JEL Codes: Q23, Q54, L73, Q02, Q17

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“…It means that, as pointed out by Risso et al [64], a proper vegetation index taken at key dates over the crop calendar can be used to identify specific crops such as soybean [69]. In fact, due to its ability and practicability to detect soybean areas, CEI is also used to monitor soybean plantations in the Brazilian Amazon Biome in the context of the Soy Moratorium [65,66]. For the remaining context variables A, T, W, and R, the KL divergences were 0.009, 0.002, 0.003, and 0.0001, respectively.…”

Section: Probability Image (Pi)mentioning

confidence: 99%

“…CEI values less than 0.2 are usually associated with targets with low (e.g., forest) or medium seasonality (e.g., Cerrado or pasture) [63,64]. On the other hand, CEI values greater than 0.2 are strongly associated with high seasonality targets such as annual crops like soybean [65,66]. Based on this knowledge, we empirically defined four intervals for L, as presented in Figure 6b.…”

Section: Discretization and Probability Functionsmentioning

confidence: 99%

“…For example, if one is looking for soybean areas for environmental supervision of soybean plantations in recent deforested areas, as defined in the Soy Moratorium context in Brazil [65,66], then areas where the probability of presence of soybean is high could be prioritized and the PI could be used to guide the logistics of field inspection by regulatory agencies [19]. The PI can also be used as input for classifiers (e.g., as prior probability for the maximum likelihood classifier) or to mask out low probability areas before running a classification.…”

Section: Creating Thematic Maps From the Pimentioning

confidence: 99%

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Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Mello

Risso²,

Atzberger

et al. 2013

Remote Sensing

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This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts' knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a OPEN ACCESSRemote Sens. 2013, 5 6000 source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet.

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Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

Cited by 30 publications

References 44 publications

Analysis of Water Resources in Horqin Sandy Land Using Multisource Data from 2003 to 2010

Analysis of Water Resources in Horqin Sandy Land Using Multisource Data from 2003 to 2010

Trading Forests: Quantifying the Contribution of Global Commodity Markets to Emissions from Tropical Deforestation

Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

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