Environmental Object Recognition in a Natural Image

Aryal, Jagannath; Josselin, Didier

doi:10.4018/ijaeis.2014010101

Cited by 4 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose a framework that can integrate thematic parameters contributing for fire in hierarchies (Aryal and Josselin, 2014;Blaschke et al 2014 The above visualisations (Figure 4 and 5) show alignment of fire ground truth data in many places with MODIS product for both the study area. However, uncertainty is further visualised with the aid of colour composite, difference maps and error maps.…”

Section: Methodology: Multiple-scale Approach and Proposed Conceptual Frameworkmentioning

confidence: 92%

Quantifying bushfire mapping uncertainty using single and multiscale approach: a case study from Tasmania, Australia

Aryal

Louvet

2016

GEOBIA 2016: Solutions and Synergies

Self Cite

View full text Add to dashboard Cite

More than 72,000 hectares of western Tasmania were burnt in 2016 due to bushfires. Bushfires in Tasmania has high social, economical, and environmental impacts. The remote delineation of these bushfires has paramount importance for decision-making authorities to help people in emergencies and planning. Considering the fact that delineation uncertainty from Earth Observation [EO] data is inevitable, this study uses MODIS, Landsat and Sentinel-2 imageries covering the 2016 burnt areas from Tasmania. We test the hypothesis that the difference in Normalised Difference Vegetation Index (NDVI) before and after the fire event can detect the accurate delineation of burnt areas and hence the changes. MODIS, Landsat and Sentinel-2 products before and after fire are used independently in delineating and mapping bushfire boundaries. We map in three thematic classes burnt, damaged and both. Delineated boundaries are examined for uncertainty and error maps are produced. The uncertainty examination and validation are performed using ground truth data obtained from local fire authorities. Developed error metrics are used to obtain statistical measures like sensitivity, specificity, positive predictive value, negative predictive value, kappa coefficient and overall accuracy. Our results show that there is minimal difference in overall accuracy from both the sensors MODIS: [0.94 vs 0.92] and Sentinel [0.94 vs 0.93] for the classes burnt & damaged vs only burnt. Furthermore, we propose a conceptual framework for bushfire mapping uncertainty in a multiple-scale environment incorporating sensitive thematic parameters that could affect initiation of fire and blaze direction.

show abstract

Section: Methodology: Multiple-scale Approach and Proposed Conceptual Frameworkmentioning

confidence: 92%

Quantifying bushfire mapping uncertainty using single and multiscale approach: a case study from Tasmania, Australia

Aryal

Louvet

2016

GEOBIA 2016: Solutions and Synergies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 2) with a pixel resolution of 2 m from the Institut Géographique National (IGN, French mapping and survey agency) web portal. It is an interesting location, because there are distinct land uses: forest, meadows, copses, alpine ski runs, pathways, road, fallen rocks and chalets [35]. Also, there are little vs large, long linear vs more compact objects.…”

Section: Materials and Datamentioning

confidence: 99%

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Josselin¹,

Louvet²

2019

IJGI

Self Cite

View full text Add to dashboard Cite

Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable Areal Unit Problem. Subsequently, even in a same spatial partition, there may be a bias in statistics describing the objects due to some size effect of the pixel samples. For instance, pixels homogeneity based on Grey Level Cooccurrence Matrices (GLCM), Landscape Shape Index, entropy, object compacity, perimeter/area ratio are studied according to scale. The approach consists in studying the behavior of a given statistical metrics through scales and to compare the results on several image segmentations, according to different partitioning processes, from GEOBIA (Baatz & Schäpe algorithm and Self Organizing Maps) or using reference grids. We finally discuss about the relationship between GEOBIA metrics and scale. By analysing object shape and pixels composition from different metrics points of views, we show that GEOBIA does not always mitigate the Modifiable Areal Unit Problem.

show abstract

“…The classification system developed by the International Geosphere-Biosphere Programme (IGBP) was adopted to classify the research into 17 categories [26,27]. Land-use extraction was achieved through the object-oriented classification [28][29][30] module in ENVI5.3 software (Exelis Visual Information Solutions, Inc., Broomfield, CO, USA).…”

Section: Data Preprocessingmentioning

confidence: 99%

Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

Fan

Wang

et al. 2018

Sustainability

View full text Add to dashboard Cite

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

show abstract

Environmental Object Recognition in a Natural Image

Cited by 4 publications

References 24 publications

Quantifying bushfire mapping uncertainty using single and multiscale approach: a case study from Tasmania, Australia

Quantifying bushfire mapping uncertainty using single and multiscale approach: a case study from Tasmania, Australia

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

Contact Info

Product

Resources

About