“All That’s Left is Bare Land and Sky”: Palm Oil Culture and Socioenvironmental Impacts on a Tembé Indigenous Territory in the Brazilian Amazon

Abstract The expansion of large-scale oil palm cultivation in the Amazon has exposed indigenous populations to changes around their lands and in their daily activities. Given the lack of studies on the impacts of oil palm on these populations, this study analyzed the socio-environmental impacts perceived by the Tembé, in Tomé-Açu, Pará State. With an interdisciplinary approach, between 2016 and 2017, semi-structured interviews and participant observation were conducted in five villages, inside and adjacent to the Turé-Mariquita Indigenous Land and complemented by remote sensing analyzes. This article describes the pressure vectors and the main socio-environmental impacts through the analysis of the indigenous narrative, and then these are discussed based on the scientific literature and remote sensing data. The findings indicate that the conversion of the surrounding territory to oil palm monoculture generated social and environmental changes that negatively affected the Tembé’s way of life and their capacity of socio-cultural reproduction.

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THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

Proceedings of International Conference "Managinag risks to coastal regions and communities in a changinag world" (EMECS'11 - SeaCoasts XXVI) ◽

10.21610/conferencearticle_58b431552d3e8 ◽

2017 ◽

Author(s):

Nikifor Ostanin ◽

Nikifor Ostanin

Keyword(s):

Remote Sensing ◽

Coastal Zone ◽

Environmental Changes ◽

Rapid Development ◽

Water Environment ◽

Remote Sensing Data ◽

Remotely Sensed ◽

Gulf Of Finland ◽

Coastal Protection ◽

Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

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LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

Remote Sensing of Environment ◽

10.1016/j.rse.2018.11.036 ◽

2019 ◽

Vol 221 ◽

pp. 695-706 ◽

Cited By ~ 12

Author(s):

Jianbo Qi ◽

Donghui Xie ◽

Tiangang Yin ◽

Guangjian Yan ◽

Jean-Philippe Gastellu-Etchegorry ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Remote Sensing Data ◽

Image Simulation ◽

Simulation Framework ◽

Sensing Data

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Remote Sensing for Property Valuation: A Data Source Comparison in Support of Fair Land Taxation in Rwanda

Remote Sensing ◽

10.3390/rs13183563 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3563

Author(s):

Mila Koeva ◽

Oscar Gasuku ◽

Monica Lengoiboni ◽

Kwabena Asiama ◽

Rohan Mark Bennett ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Aerial Images ◽

Primary Data ◽

Structured Interviews ◽

Remotely Sensed Data ◽

Quality Of Data ◽

New Approach ◽

Property Valuation ◽

Land Administration

Remotely sensed data is increasingly applied across many domains, including fit-for-purpose land administration (FFPLA), where the focus is on fast, affordable, and accurate property information collection. Property valuation, as one of the main functions of land administration systems, is influenced by locational, physical, legal, and economic factors. Despite the importance of property valuation to economic development, there are often no standardized rules or strict data requirements for property valuation for taxation in developing contexts, such as Rwanda. This study aims at assessing different remote sensing data in support of developing a new approach for property valuation for taxation in Rwanda; one that aligns with the FFPLA philosophy. Three different remote sensing technologies, (i) aerial images acquired with a digital camera, (ii) WorldView2 satellite images, and (iii) unmanned aerial vehicle (UAV) images obtained with a DJI Phantom 2 Vision Plus quadcopter, are compared and analyzed in terms of their fitness to fulfil the requirements for valuation for taxation purposes. Quantitative and qualitative methods are applied for the comparative analysis. Prior to the field visit, the fundamental concepts of property valuation for taxation and remote sensing were reviewed. In the field, reference data using high precision GNSS (Leica) was collected and used for quantitative assessment. Primary data was further collected via semi-structured interviews and focus group discussions. The results show that UAVs have the highest potential for collecting data to support property valuation for taxation. The main reasons are the prime need for accurate-enough and up-to-date information. The comparison of the different remote sensing techniques and the provided new approach can support land valuers and professionals in the field in bottom-up activities following the FFPLA principles and maintaining the temporal quality of data needed for fair taxation.

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An efficient organization method for large-scale and long time-series remote sensing data in a cloud computing environment

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3110900 ◽

2021 ◽

pp. 1-1

Author(s):

Jining Yan ◽

Yuanxing Liu ◽

Lizhe Wang ◽

Zhipeng Wang ◽

Xiaohui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Cloud Computing ◽

Large Scale ◽

Remote Sensing Data ◽

Computing Environment ◽

Cloud Computing Environment ◽

Efficient Organization ◽

Sensing Data ◽

Long Time

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Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

RBRH ◽

10.1590/2318-0331.241920180127 ◽

2019 ◽

Vol 24 ◽

Author(s):

Hugo de Oliveira Fagundes ◽

Fernando Mainardi Fan ◽

Rodrigo Cauduro Dias de Paiva

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Suspended Sediment ◽

Large Scale ◽

Suspended Sediment Concentration ◽

Remote Sensing Data ◽

Sediment Concentration ◽

Spatial Discretization ◽

Calibration And Validation ◽

Sediment Model

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

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