Wetland Quality Assessment Using Landsat Imagery and GIS

Ecological environment quality assessment of Xishuangbanna rubber plantations expansion (1995–2018) based on multi-temporal Landsat imagery and RSEI

Geocarto International ◽

10.1080/10106049.2020.1861663 ◽

2021 ◽

pp. 1-28

Author(s):

Yuan Xiong ◽

Weiheng Xu ◽

Shaodong Huang ◽

Chao Wu ◽

Fei Dai ◽

...

Keyword(s):

Quality Assessment ◽

Landsat Imagery ◽

Ecological Environment ◽

Rubber Plantations ◽

Environment Quality ◽

Multi Temporal

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Quality Assessment of Heterogeneous Training Data Sets for Classification of Urban Area with Landsat Imagery

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.5.339 ◽

2021 ◽

Vol 87 (5) ◽

pp. 339-348

Author(s):

Neema Nicodemus Lyimo ◽

Fang Luo ◽

Qimin Cheng ◽

Hao Peng

Keyword(s):

Quality Assessment ◽

Open Source ◽

Urban Area ◽

Landsat Imagery ◽

Training Data ◽

Data Sources ◽

Data Sets ◽

Spectral Distance ◽

Training Samples ◽

Open Government Data

Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification.

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INVESTIGATIONS ON THE GLOBAL IMAGE DATASETS FOR THE ABSOLUTE GEOMETRIC QUALITY ASSESSMENT OF MSG SEVIRI IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1339-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1339-1346

Author(s):

S. Kocaman ◽

V. Debaecker ◽

S. Bas ◽

S. Saunier ◽

K. Garcia ◽

...

Keyword(s):

Quality Assessment ◽

Reference Data ◽

Accuracy Assessment ◽

Landsat Imagery ◽

Eu Member States ◽

Spectral Bands ◽

Satellite Sensors ◽

Global Coverage ◽

Geometric Quality ◽

Earth Observation Satellites

Abstract. EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites), an intergovernmental organisation founded in 1986, supplies weather and climate-related satellite data, images and products throughout the year for EU Member States and other users worldwide. The optical Earth Observation satellites launched and operated by EUMETSAT, both current and planned ones, have different spatial, spectral and temporal resolutions; sensor models and acquisition geometries. While the number and the diversity of the satellite missions increase, the requirement of novel methods and up-to-date reference data for geometric accuracy assessment of the imagery also grows. This paper aims at reporting the results of a study investigating the availability for suitable satellite imagery to be employed as reference data for the geometric quality assessment (GQA) of MSG SEVIRI Level 1.5 image products. The reference datasets need to have superior spatial resolution, wide global coverage, and spectral compatibility with respect to the SEVIRI sensor, which has 12 spectral bands with 1 km and 3 km spatial resolutions. The SEVIRI sensor works with whiskbroom principle at a geostationary orbit and collects data at 5 minutes (rapid scan) and 15-minutes (full scan) intervals. Although preliminary investigations on reference data were performed by using images of different satellite sensors during the study, in-depth investigations were performed with MERIS global image mosaic and Landsat imagery. The progress and different problems observed in the images are reported here.

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