Land Classification Research

2017 ◽  
pp. 242-253
Author(s):  
Michael N. DeMers
Keyword(s):  
Remote Sensing ◽  
Error Analysis ◽  
Research Agenda ◽  
Classification Error ◽  
Technological Changes ◽  
Land Classification ◽  
Geographic Scale ◽  
Economic Implications ◽  
Wide Range ◽  
Digital Pixel

Land classification is so central to geography that its use, and the use of its derivative and corresponding products, is seldom even questioned. Since its earliest implementations land classification has adapted to changes in geographic scale and in the nature of the categorical systematics upon which it is based. Land classification has changed in its techniques and in how it adapts to technological changes, particularly those related to remote sensing and geographic information systems. The adaptation of land classification to digital pixel-based classification spawned a wide range of land classification error analysis techniques. These techniques do not easily transfer to non-pixel based classification error analysis as recent research on rapid land assessment methodologies and land change error analysis has shown. This disparity suggests a need to reevaluate the very nature of land classification research. To begin such an evaluation, this lecture provides a retrospective on the roots of land classification research, examines some of the milestones of that research, and describes the divergent paths such research has taken. It examines the importance of land classification in these times of ever decreasing global resources, and reviews its potential legal, social, and economic implications. Based on this retrospective, this paper advances the need for geographic researchers to envision land classification not only as a set of techniques, but more generally to focus on systematic geography in all its facets as a research agenda in its own right.

Land Classification Research

2014 ◽  
Vol 5 (3) ◽  
pp. 82-92 ◽  
Author(s):  
Michael N. DeMers
Keyword(s):  
Error Analysis ◽  
Research Agenda ◽  
Classification Error ◽  
Technological Changes ◽  
Land Classification ◽  
Geographic Scale ◽  
Economic Implications ◽  
Analysis Techniques ◽  
Wide Range ◽  
Digital Pixel

Land classification is so central to geography that its use, and the use of its derivative and corresponding products, is seldom even questioned. Since its earliest implementations land classification has adapted to changes in geographic scale and in the nature of the categorical systematics upon which it is based. Land classification has changed in its techniques and in how it adapts to technological changes, particularly those related to remote sensing and geographic information systems. The adaptation of land classification to digital pixel-based classification spawned a wide range of land classification error analysis techniques. These techniques do not easily transfer to non-pixel based classification error analysis as recent research on rapid land assessment methodologies and land change error analysis has shown. This disparity suggests a need to reevaluate the very nature of land classification research. To begin such an evaluation, this lecture provides a retrospective on the roots of land classification research, examines some of the milestones of that research, and describes the divergent paths such research has taken. It examines the importance of land classification in these times of ever decreasing global resources, and reviews its potential legal, social, and economic implications. Based on this retrospective, this paper advances the need for geographic researchers to envision land classification not only as a set of techniques, but more generally to focus on systematic geography in all its facets as a research agenda in its own right.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals An Overview of Flood Risk Analysis Methods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 474
Author(s):  
Daniel Constantin Diaconu ◽  
Romulus Costache ◽  
Mihnea Cristian Popa
Keyword(s):  
Core Collection ◽  
Web Of Science ◽  
Research Directions ◽  
Economic Implications ◽  
Forecasting Methods ◽  
Wide Range ◽  
Scientific Papers ◽  
Recent Developments ◽  
Flood Analysis ◽  
Temporal And Spatial

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

Shoreline Change Analysis along the Udupi-Dakshina Kannada Coastal Stretch in Karnataka, West Coast of India using DSAS

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
N.A. Anjita ◽  
G.S. Dwarakish
Keyword(s):  
Remote Sensing ◽  
West Coast ◽  
Shoreline Change ◽  
Sediment Supply ◽  
Shoreline Changes ◽  
West Coast Of India ◽  
Erosion Rates ◽  
Wide Range ◽  
Cyclic Changes ◽  
Accretion And Erosion

Study of morphological variations and the effects of oceanographic processes such as erosion and accretion at different temporal scales are important to understand the nature of the coast and the cyclic changes occurring during different seasons. The Udupi-Dakshina Kannada coast along the west coast of India exhibits a wide range of changes depending on the interactions of tide and wave energy, sediment supply and more importantly human intervention. In view of this, the present work has been carried out to study the changes in shoreline changes along the Udupi-Dakshina Kannada coast over a period of 29 years from 1990 to 2019. Remote Sensing and GIS techniques have been used to demarcate shorelines and calculate the shoreline change rates. Overall accretion and erosion rates were found to be 1.28 m/year and 0.91 m/year respectively along the coast. Highest accretion and erosion rates of 12.57 m/year and 5.34 m/year was noticed along the Dakshina Kannada coast. The study also suggests that multi-dated satellite data along with statistical techniques can be effectively used for prediction of shoreline changes. Keywords: remote sensing, GIS, Dakshina Kannada coast, oceanography, shoreline.

From inflow to interflow, through plunging and lofting: uncovering the dominant flow processes of a sediment-rich negatively buoyant river inflow into a stratified lake

2021 ◽  
Author(s):  
Stan Thorez ◽  
Koen Blanckaert ◽  
Ulrich Lemmin ◽  
David Andrew Barry
Keyword(s):  
Remote Sensing ◽  
River Mouth ◽  
Ambient Water ◽  
Remote Sensing Images ◽  
Reservoir Water ◽  
Neutral Buoyancy ◽  
Wide Range ◽  
Adcp Measurements ◽  
Vorticity Production ◽  
Negatively Buoyant

<p>Lake and reservoir water quality is impacted greatly by the input of momentum, heat, oxygen, sediment, nutrients and contaminants delivered to them by riverine inflows. When such an inflow is negatively buoyant, it will plunge upon contact with the receiving ambient water and form a gravity-driven current near the bed (density current). If such a current is sediment-laden, its bulk density can be higher than that of the surrounding ambient water, even if its carrying fluid has a density lower than that of the surrounding ambient water. After sufficient sediment particles have settled however, the buoyancy of the current can reverse and lead to the plume rising up from the bed, a process referred to as lofting. In a stratified environment, the river plume may then find its way into a layer of neutral buoyancy to form an intermediate current (interflow). A deeper understanding of the wide range of hydrodynamic processes related to the transitions from open-channel inflow to underflow (plunging) and from underflow to interflow (lofting) is crucial in predicting the fate of all components introduced into the lake or reservoir by the inflow.</p><p>Field measurements of the plunging inflow of the negatively buoyant Rhône River into Lake Geneva (Switzerland/France) are presented. A combination of a vessel-mounted ADCP and remote sensing cameras was used to capture the three-dimensional flow field of the plunging and lofting transition zones over a wide range of spatial and temporal scales.</p><p>In the plunge zone, the ADCP measurements show that the inflowing river water undergoes a lateral (perpendicular to its downstream direction) slumping movement, caused by its density surplus compared to the ambient lake water and the resulting baroclinic vorticity production. This effect is also visible in the remote sensing images in the form of a distinct plume of sediment-rich water with a triangular shape leading away from the river mouth in the downstream direction towards a sharp tip. A wide range of vortical structures, which most likely impact the amount of mixing taking place, is also visible at the surface in the plunging zone.</p><p>In the lofting zone, the ADCP measurements show that the underflow undergoes a lofting movement at its edges. This is most likely caused by a higher sedimentation rate due to the lower velocities at the underflow edges and leads to a part of the underflow peeling off and forming an interflow, while the higher velocity core of the underflow continues following the bed. Here, the baroclinic vorticity production works in the opposite direction as that in the plunge zone. Further downstream, as more particles have settled and the surrounding ambient water has become denser, the remaining underflow also undergoes a lofting motion. The remnants of these lofting processes show in the remote sensing images as intermittent ‘boils’ of sediment rich water reaching the surface and traces of surface layer leakage.</p>

scholarly journals Helping auditors identify deception through psycholinguistics

2018 ◽  
Vol 25 (4) ◽  
pp. 1062-1076 ◽  
Author(s):  
Rebecca Nicolaides ◽  
Richard Trafford ◽  
Russell Craig
Keyword(s):  
Oral Language ◽  
Research Agenda ◽  
Relevant Literature ◽  
Bibliographic Databases ◽  
Future Research ◽  
Content Type ◽  
Fraud Risk ◽  
Critical Elements ◽  
Future Research Agenda ◽  
Wide Range

Purpose This paper reviews an array of psycholinguistic techniques that auditors can deploy to explore written and oral language for signs of deception. The review is drawn upon to propose some elements of a forward research agenda. Design/methodology/approach Relevant literature across several disciplines is identified through keyword searches of major bibliographic databases. Findings The techniques highlighted have considerable potential for use by auditors to identify audit contexts which merit closer audit investigation. However, the techniques need further contextual empirical investigation in audit contexts. Seven specific propositions are presented for empirical testing. Originality/value This paper assembles literature on deceptive communication from a wide range of disciplines and relates it to the audit context. Auditors’ attention is directed to potential linguistic signals of fraud risk, and opportunities for future research are suggested. The paper is consciousness-raising, has pedagogic purpose and suggests critical elements for a future research agenda.

Sign in / Sign up

Export Citation Format

Share Document