Avoided deforestation in Brazilian Amazonia: Simulating the effect of the Juma Sustainable Development Reserve

Yanai, Aurora Miho; Fearnside, Philip M.; Graça, Paulo Maurı́cio Lima de Alencastro; Nogueira, Euler Melo

doi:10.1016/j.foreco.2012.06.029

Cited by 47 publications

(41 citation statements)

References 36 publications

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“…To analyze the dynamics of deforestation we used the software DINAMICA EGO, which involves a multi-step stochastic simulation with dynamic spatial transition probabilities to reproduce the dimensions and forms of landscape change [26][27][28][29]. DINAMICA has been widely used for different purposes, among others: simulating spatial patterns of land use through the expansion of the colonization frontiers in Amazonia [26]; and modeling future land use change scenarios in Brazilian savannas [30] and national parks [31], Kenya's Eastern Arc Mountains [32], and tropical forest in Lao People's Democratic Republic [33].…”

Section: Land Use Change Spatial Modelmentioning

confidence: 99%

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

Chadid

Dávalos²,

Molina

et al. 2015

Forests

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Abstract:The loss of tropical forests has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. Here, we examine the conversion of forests to pastures and coca crops (illicit activity) on the San Lucas Mountain Range, Colombia for 2002-2007 and 2007-2010. Land use maps and biophysical variables were used as inputs to generate land use and cover change (LUCC) models using the DINAMICA EGO software. These analyses revealed a dramatic acceleration of the pace of deforestation in the region, with rates of conversion from forest to pasture doubling from the first to the second period. Altitude, distance to other crops, and distance to rivers were the primary drivers of deforestation. The influence of these drivers, however, differed markedly depending on whether coca cultivation or pastures replaced forest. Conversion to coca was more probable farther from other crops and from settlements. In contrast, proximity to other crops and to settlements increased conversion to pasture. These relationships highlight the different roles of coca and pastures in forest loss, OPEN ACCESSForests 2015, 6 3829 with coca tending to open up new forest frontiers, and pastures tending to consolidate agricultural expansion and urban influence. Large differences between LUCC processes for each period suggest highly dynamic changes, likely associated with shifting underlying causes of deforestation. These changes may relate to shifts in demand for illicit crops, land, or mining products; however, the data to test these hypotheses are currently lacking. More frequent and detailed monitoring is required to guide actions to decrease the loss of forest in this highly vulnerable biodiversity hotspot in the Northern Andes.

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Section: Land Use Change Spatial Modelmentioning

confidence: 99%

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

Chadid

Dávalos²,

Molina

et al. 2015

Forests

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“…Because of constant change of NPP among terrestrial ecosystems or LULC types [8], an accurate LULC map is very important to support a precise estimation of NPP or carbon sequestration. The high resolution LULC maps play critical roles on the issues of: 1) reducing emission from deforestation and forest degradation (REDD) [20]- [22], 2) presenting accurate large-scale LULC map and predicting LULC changes [23]- [25], 3) detecting the response of vegetation to environmental factors and estimating the spatiotemporal variations of NPP at multiple spatial scales [8], 4) predicting land surface temperature [26], and 5) calculating the large-scale/subcanopy-based heterogeneous evapotranspiration [27].…”

Section: Introductionmentioning

confidence: 99%

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Lin

Tsogt

et al. 2015

Information Processing in Agriculture

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Changes of Land Use and Land Cover (LULC) affect atmospheric, climatic, and biological spheres of the earth. Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction. This paper examined effects of pansharpening and atmospheric correction on LULC classification. Object-Based Support Vector Machine (OB-SVM) and Pixel-Based Maximum LikelihoodClassifier (PB-MLC) were applied for LULC classification. Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image. Nevertheless, pansharpening plays much more important roles on the classification accuracy than the atmospheric correction. It can help to increase classification accuracy by 12% on average compared to the ones without pansharpening. PB-MLC and OB-SVM achieved similar classification rate. This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82% and 89% respectively. A combination of atmospheric correction, pansharpening, and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

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“…There were three papers that contributed models that we were able to submit to our validation exercise (out of 21 models from the Brazilian Amazon), two of which comprised a complete time series of model predictions (Soares‐Filho et al ., ; Yanai et al ., ), whereas the other comprised a single map of predicted tropical LULC at the end of the model prediction period (Wassenaar et al ., ). Different model scenarios, such as the Business as Usual and Governance scenarios of Soares‐Filho et al .…”

Section: Resultsmentioning

confidence: 99%

“…(), and the Baseline, with leakage and with reduced leakage scenarios of Yanai et al . () were treated as separate models in our validations, giving a total of five models. We hasten to add that were able to obtain predicted outputs from an additional 4 of the 21 models in the Brazilian Amazon, but the predicted maps were available only for a time that has not yet occurred and so could not be included in this analysis (e.g.…”

Section: Resultsmentioning

confidence: 99%

The transparency, reliability and utility of tropical rainforest land-use and land-cover change models

2014

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Land-use and land-cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land-use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline.

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Avoided deforestation in Brazilian Amazonia: Simulating the effect of the Juma Sustainable Development Reserve

Cited by 47 publications

References 36 publications

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

The transparency, reliability and utility of tropical rainforest land-use and land-cover change models

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