Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development

Building a hybrid land cover maps with crowdsourcing and geographically weighted regression

Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development

Downgrading recent estimates of land available for biofuel production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.Fil: Fritz, Stephen. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: See, Linda. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: van der Velde, Marijn. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Nalepa, Rachel A.. Boston University; Estados UnidosFil: Perger, Christoph. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Schill, Christian. Universitàdi Modena e Reggio Emilia. Dipartimento di Scienze delle Terra; ItaliaFil: McCallum, Ian. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Dmitry Schepaschenko. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Kraxner, Florian. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Cai, Ximing. University of Illinois at Urbana; Estados UnidosFil: Zhang, Xiao. University of Illinois at Urbana; Estados UnidosFil: Ortner, Simone. University of Applied Sciences; AustriaFil: Hazarika, Rubul. Gauhati University; IndiaFil: Cipriani, Anna. Universitàdi Modena e Reggio Emilia. Dipartimento di Scienze delle Terra; Italia. Columbia University; Estados UnidosFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rabia, Ahmed H.. Università degli Studi di Napoli Federico II; ItaliaFil: García, Alfredo Gabriel. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vakolyuk, Mar'yana. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Singha, Kuleswar. Gauhati University; IndiaFil: Beget, María Eugenia. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Erasmi, Stefan. Universität Göttingen; AlemaniaFil: Albrecht, Franziska. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Shaw, Brian. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; AustriaFil: Obersteiner, Michael. International Institute of Applied Systems Analysis. Ecosystem Services and Management Program; Austri

Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development