The bio‐geophysical approach to remote sensing of vegetation in coupled human‐environment systems – societal benefits and global context

2006 ◽  
Vol 51 (2) ◽  
pp. 49-66 ◽  
Author(s):  
M. J. Hill ◽  
G. P. Asner ◽  
A. A. Held
Keyword(s):  
Remote Sensing ◽  
Human Environment ◽  
Global Context ◽  
Societal Benefits

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

GIS and Remote Sensing in Environmental Risk Assessment

2011 ◽  
pp. 840-847 ◽  
Author(s):  
X. Mara Chen
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Environmental Protection ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Global Economy ◽  
Environmental Risks ◽  
Human Environment ◽  
Gis And Remote Sensing ◽  
Risk Assessment And Management

The existence, well-being, and sustainable development of the global economy hinges upon the state of the earth’s environment. Effective environmental risk assessment and management issues have become increasingly important. With the ever-growing global population and expanding economic development, we consume more natural resources, produce more waste, and develop more areas into the regions that are prone to environmental risks. Although humans have interacted with the environment for thousands of years, environmental risk assessment and management is only a recent research undertaking. As the industrialization has made the human-environment interactions more dynamic and complex, the increased environmental risks have propelled and compelled people to use technologies for identifying and solving problems. The earliest global environmental applications of remote sensing and GIS technologies began in the 1960s, particularly marked by the successful launch of the TIROS- 1, the first meteorological satellite, and the development of computer-based geographic information systems (GIS). The story Silent Spring (Carson, 1962) awoke the public’s environmental consciousness and promoted the public demands for governments to set up environmental protection policies and research priorities. The birth of the U.S. Environmental Protection Agency (EPA) in 1970 set the stage for modern environment risk assessment. The launch of the LANDSAT program in 1972 created a new way for monitoring global land use and land cover changes (Foley, 1999; Goward, Masek, Williams, Irons, & Thompson, 2001).

scholarly journals Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features

2018 ◽  
Vol 10 (5) ◽  
pp. 734 ◽  
Author(s):  
Dan Zeng ◽  
Shuaijun Chen ◽  
Boyang Chen ◽  
Shuying Li
Keyword(s):  
Remote Sensing ◽  
Scene Classification ◽  
Global Context ◽  
Object Features

scholarly journals Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

2021 ◽  
Vol 9 ◽  
Author(s):  
Dylan S. Davis ◽  
Kristina Douglass
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Evolutionary Dynamics ◽  
Environmental Changes ◽  
Spatial Scales ◽  
Small Scale ◽  
Environment Interaction ◽  
Human Environment ◽  
Landscape Modification ◽  
Geochemical Properties

Archaeologists interested in the evolution of anthropogenic landscapes have productively adopted Niche Construction Theory (NCT), in order to assess long-term legacies of human-environment interactions. Applications of NCT have especially been used to elucidate co-evolutionary dynamics in agricultural and pastoral systems. Meanwhile, foraging and/or highly mobile small-scale communities, often thought of as less intensive in terms of land-use than agropastoral economies, have received less theoretical and analytical attention from a landscape perspective. Here we address this lacuna by contributing a novel remote sensing approach for investigating legacies of human-environment interaction on landscapes that have a long history of co-evolution with highly mobile foraging communities. Our study is centered on coastal southwest Madagascar, a region inhabited by foraging and fishing communities for close to two millennia. Despite significant environmental changes in southwest Madagascar’s environment following human settlement, including a wave of faunal extinctions, little is known about the scale, pace and nature of anthropogenic landscape modification. Archaeological deposits in this area generally bear ephemeral traces of past human activity and do not exhibit readily visible signatures of intensive land-use and landscape modification (e.g., agricultural modifications, monumental architecture, etc.). In this paper we use high-resolution satellite imagery and vegetative indices to reveal a legacy of human-landscape co-evolution by comparing the characteristics – vegetative productivity and geochemical properties – of archaeological sites to those of locations with no documented archaeological materials. Then, we use a random forest (RF) algorithm and spatial statistics to quantify the extent of archaeological activity and use this analysis to contextualize modern-day human-environment dynamics. Our results demonstrate that coastal foraging communities in southwest Madagascar over the past 1,000 years have extensively altered the landscape. Our study thus expands the temporal and spatial scales at which we can evaluate human-environment dynamics on Madagascar, providing new opportunities to study early periods of the island’s human history when mobile foraging communities were the dominant drivers of landscape change.

scholarly journals A Transformative Concept: From Data Being Passive Objects to Data Being Active Subjects

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 135 ◽  
Author(s):  
Hans-Peter Plag ◽  
Shelley-Ann Jules-Plag
Keyword(s):  
Data Dissemination ◽  
Human Environment ◽  
Earth Observations ◽  
Data Usage ◽  
Semantic Data ◽  
Societal Benefits ◽  
Comprehensive Knowledge ◽  
User Communities ◽  
Comprehensive Picture ◽  
Core Elements

The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive objects (DPO) to a new perception of data as active subjects (DAS). This transition would greatly increase data usage and exploitation, and support the extraction of knowledge from data products. Enabling the data subjects to actively reach out to potential users would revolutionize data dissemination and sharing and facilitate collaboration in user communities. The three core elements of the transformative DAS concept are: (1) “intelligent semantic data agents” (ISDAs) that have the capabilities to communicate with their human and digital environment. Each ISDA provides a voice to the data product it represents. It has comprehensive knowledge of the represented product including quality, uncertainties, access conditions, previous uses, user feedbacks, etc., and it can engage in transactions with users. (2) A knowledge base that constructs extensive graphs presenting a comprehensive picture of communities of people, applications, models, tools, and resources and provides tools for the analysis of these graphs. (3) An interaction platform that links the ISDAs to the human environment and facilitates transaction including discovery of products, access to products and derived knowledge, modifications and use of products, and the exchange of feedback on the usage. This platform documents the transactions in a secure way maintaining full provenance.

GIS and Remote Sensing in Environmental Risk Assessment

2011 ◽  
pp. 1659-1663
Author(s):  
X. Mara Chen
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Environmental Protection ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Global Economy ◽  
Environmental Risks ◽  
Human Environment ◽  
Gis And Remote Sensing ◽  
Risk Assessment And Management

The existence, well-being, and sustainable development of the global economy hinges upon the state of the earth’s environment. Effective environmental risk assessment and management issues have become increasingly important. With the ever-growing global population and expanding economic development, we consume more natural resources, produce more waste, and develop more areas into the regions that are prone to environmental risks. Although humans have interacted with the environment for thousands of years, environmental risk assessment and management is only a recent research undertaking. As the industrialization has made the human-environment interactions more dynamic and complex, the increased environmental risks have propelled and compelled people to use technologies for identifying and solving problems. The earliest global environmental applications of remote sensing and GIS technologies began in the 1960s, particularly marked by the successful launch of the TIROS- 1, the first meteorological satellite, and the development of computer-based geographic information systems (GIS). The story Silent Spring (Carson, 1962) awoke the public’s environmental consciousness and promoted the public demands for governments to set up environmental protection policies and research priorities. The birth of the U.S. Environmental Protection Agency (EPA) in 1970 set the stage for modern environment risk assessment. The launch of the LANDSAT program in 1972 created a new way for monitoring global land use and land cover changes (Foley, 1999; Goward, Masek, Williams, Irons, & Thompson, 2001).

How global dryland vegetation dynamics relate to changing climatic conditions and anthropogenic dynamics

2020 ◽  
Author(s):  
Christin Abel ◽  
Stéphanie Horion ◽  
Torbern Tagesson ◽  
Wanda De Keersmaecker ◽  
Alistair W.R. Seddon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Large Scale ◽  
Population Expansion ◽  
Remote Sensing Data ◽  
Well Being ◽  
Climatic Conditions ◽  
Ecosystem Dynamics ◽  
Human Environment ◽  
Positive Effects

<p>Monitoring ecosystem dynamics is fundamental to understanding and eventually forecasting ecosystem states. To achieve this, it is crucial to identify and understand potential negative/ positive effects from a changing world on the system. As one key aspect of every ecosystem are the living organisms it involves, our research focuses on vegetation, since it has major implications for both the climate, because plants absorb carbon dioxide, and human well-being, because people depend on the products of plants. Specifically addressing global drylands, where vegetation productivity is tightly linked to the availability of water (mainly through rainfall), we quantify changes in vegetation functioning by analyzing the slopes of a sequential linear regression (SeRGS) over a time series of remote sensing data (NDVI and rainfall), as introduced in Abel et al., 2019. Further, we apply a data-driven, empirical approach to estimate the relative importance of potential drivers of identified changes, as in Abel et al., 2020 (in revision). We show that there are substantial regional and continental differences in vegetation functioning and that these trends can be linked to global trends of population expansion, large-scale agriculture intensification/ expansion and changing climatic conditions. Results from these studies, follow-up research and perspectives will be presented and discussed at EGU.</p><p>References:</p><p>Abel, C., Horion, S., Tagesson, T., Brandt, M., Fensholt, R. (2019). Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS). Remote Sens. Environ. 224, 317–332.</p><p>Abel, C., Horion, S., Tagesson, T., De Keersmaecker, W., Seddon, A. W. R., Abdi A. M., Fensholt, R. (2020). How the human-environment nexus changes global dryland vegetation functioning, in revision.</p>

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