Majuli at the Crossroads: A Study of Cultural Geomorphology

Although well established elsewhere, cultural geomorphology has not yet been well-grounded in Northeast India where a perceived dearth of studies in this sub-branch of geography exists. The Brahmaputra valley, which has a long physical and cultural history, is a unique laboratory, which offers opportunities to study anthropo-geomorphologic, achaeo-geomorphologic and cultural landscapes. The Majuli river island, ostensibly the largest island in the world, houses traditional art crafts and dances, despite being continually under the siege of a plethora of physical obstacles such as flooding, bank erosion, etc..  The present study aims at studying how the physical processes that constantly reshape the map of the island exert their influence on the socio-economic and cultural milieu of the region. The paper further analyses why despite all odds Majuli thrives and continues to preserve and maintain its rich natural and cultural heritage, in ways that are perhaps unparalleled in the region or even elsewhere in the globe

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

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services

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