Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map

2002 ◽  
Vol 81 (2-3) ◽  
pp. 443-455 ◽  
Author(s):  
M Laba ◽  
S.K Gregory ◽  
J Braden ◽  
D Ogurcak ◽  
E Hill ◽  
...  
Keyword(s):  
New York ◽  
Land Cover ◽  
Gap Analysis ◽  
Accuracy Assessment ◽  
Land Cover Map

scholarly journals Integrating Recent Land Cover Mapping Efforts to Update the National Gap Analysis Program's Species Habitat Map

2014 ◽  
Vol XL-1 ◽  
pp. 245-252 ◽  
Author(s):  
A. J. McKerrow ◽  
A. Davidson ◽  
T. S. Earnhardt ◽  
A. L. Benson
Keyword(s):  
Land Cover ◽  
Gap Analysis ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Suitable Habitat ◽  
Vertebrate Species ◽  
Distribution Models ◽  
Terrestrial Vertebrate ◽  
Landscape Characterization ◽  
Land Cover Map

Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models. <br><br> We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as "changed" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker (<i>Melanerpes lewis</i>) and the White-tailed Jack Rabbit (<i>Lepus townsendii</i>) and Brown Headed nuthatch (<i>Sitta pusilla</i>). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in 2011 was &minus;14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit (<i>Lepus townsendii</i>) had a net change of &minus;1% (11% decline, 10% gain). For that species we found the updates related to opening of forest due to burning and regenerating shrubs following harvest to be the locally important main transitions. In the Southeast updates related to timber management and urbanization are locally important.

scholarly journals EXTENDING ACCURACY ASSESSMENT PROCEDURES OF GLOBAL COVERAGE LAND COVER MAPS THROUGH SPATIAL ASSOCIATION ANALYSIS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1601-1607
Author(s):  
D. Oxoli ◽  
G. Bratic ◽  
H. Wu ◽  
M. A. Brovelli
Keyword(s):  
Land Cover ◽  
Spatial Patterns ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Spatial Association ◽  
Visual Exploration ◽  
Assessment Procedure ◽  
Lombardy Region ◽  
Land Cover Map ◽  
Land Cover Maps

<p><strong>Abstract.</strong> High-resolution land cover maps are in high demand for many environmental applications. Yet, the information they provide is uncertain unless the accuracy of these maps is known. Therefore, accuracy assessment should be an integral part of land cover map production as a way of ensuring reliable products. The traditional accuracy metrics like Overall Accuracy and Producer’s and User’s accuracies &amp;ndash; based on the confusion matrix &amp;ndash; are useful to understand global accuracy of the map, but they do not provide insight into the possible nature or source of the errors. The idea behind this work is to complement traditional accuracy metrics with the analysis of error spatial patterns. The aim is to discover errors underlying features which can be later employed to improve the traditional accuracy assessment. The designed procedure is applied to the accuracy assessment of the GlobeLand30 global land cover map for the Lombardy Region (Northern Italy) by means of comparison with the DUSAF regional land cover map. Traditional accuracy assessment quantified the classification accuracies of the map. Indeed, critical errors were pointed out and further analyses on their spatial patterns were performed by means of the Moran’s I indicator. Additionally, visual exploration of the spatial patterns was performed. This allowed describing possible sources of errors. Both software and analysis strategies were described in detail to facilitate future improvement and replication of the procedure. The results of the exploratory experiments are critically discussed in relation to the benefits that they potentially introduce into the traditional accuracy assessment procedure.</p>

scholarly journals ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 669-674 ◽  
Author(s):  
C. C. Fonte ◽  
L. See ◽  
J. C. Laso-Bayas ◽  
M. Lesiv ◽  
S. Fritz
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Reference Database ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Data Set ◽  
Mixed Pixels ◽  
Land Cover Map

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

Post Classification Using Cellular Automata for Landsat Images

2012 ◽  
Vol 433-440 ◽  
pp. 5431-5435
Author(s):  
Ebada Sarhan ◽  
Eraky Khalifa ◽  
Ayman M. Nabil
Keyword(s):  
Developing Countries ◽  
Cellular Automata ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Landsat Images ◽  
Land Cover Map ◽  
Vegetation Water ◽  
Mega Cities

The research presented in this paper aims at improving the accuracy of land-use maps produced from classification of Landsat images of mega cities in developing countries. In other words, the main objective of this paper is to find a suitable post classification technique that gives optimum results for Landsat images of mega cities in developing countries. To reach our goal, the paper presents a classification of two TM-Landsat sub scenes using a traditional statistical classifier (Maximum Likelihood) into four land cover classes (vegetation-water-Desert-Urban); then the accuracy assessment for the produced land-cover map will be calculated. Following to this step, three post processing techniques- Majority Filter, Probability label Relaxation (PLR), and Cellular Automata (CA) - will be applied in order to improve the accuracy of the previously produced land cover map. Finally, the same accuracy assessment measurements will be calculated for the two land-cover maps produced by each of the above post classification techniques. Initial results will show that CA outperformed the other techniques. In this paper we propose a methodology to implement a satellite image post classification Algorithm with cellular Automata.

scholarly journals Intercomparison of satellite-derived snow-cover maps

2000 ◽  
Vol 31 ◽  
pp. 369-376 ◽  
Author(s):  
Dorothy K. Hall ◽  
Andrew B. Tait ◽  
James L. Foster ◽  
Alfred T. C. Chang ◽  
Milan Allen
Keyword(s):  
New York ◽  
Land Cover ◽  
New England ◽  
South Dakota ◽  
Snow Cover ◽  
The United States ◽  
Mapping Algorithm ◽  
The North ◽  
Land Cover Map ◽  
North And South

AbstractIn anticipation of the launch of the Earth Observing System (EOS) Terra, and the Aqua spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectrora-diometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study areas in Canada and the United States, acquired from different sensors using different snow-mapping algorithms. Four locations are studied: (1) Saskatchewan, Canada; (2) New England (New Hampshire, Vermont and Massachusetts) and eastern New York; (3) central Idaho and western Montana; and (4) North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm from Landsat Thematic Mapper (TM) scenes of each study area at 30 m and when the TM data were degraded to 1 km resolution. U.S. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1km resolution snow maps were also used, as were snow maps derived from 0.5° × 0.5° resolution Special Sensor Microwave Imager (SSM/I) data. A land-cover map derived from the International Geosphere-Biosphere Program land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/ I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g. tree canopies, branches and trunks) are seen in the TM data but not in the coarser-resolution maps which may map the areas as completely snow-covered. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30 m resolutionTM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Results show that the changes in amount of snow cover, as compared to to the 30 m resolution TM maps, are lowest using the TM 1km resolution maps, at 0–40%. The greatest change (>100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the changes were 0–40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

scholarly journals Land Cover Change and Coastal Sustainability in the Coastal Area of Kendari Bay, Southeast Sulawesi, Indonesia

2021 ◽  
Vol 921 (1) ◽  
pp. 012008
Author(s):  
Ariyani ◽  
M Achmad ◽  
E Morgan
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Use Planning ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Coastal Areas ◽  
Maximum Likelihood Algorithm ◽  
Rapid Changes ◽  
Land Cover Map ◽  
Method Accuracy

Abstract Coastal areas provide invaluable resources which have important environment, economic and social value. These resources encourages growing population and development which induced rapid changes in coastal areas. This study aims to analyse the changes in land cover of the coastal areas of Kendari Bay to provide recent perspectives of how land cover has changed using Landsat TM and Landsat OLI images for the period of 1998, 2008 and 2018. The classified land cover classes are categorized as waterbodies, built-up, bareland, forest, wetland, vegetation and mangrove. The land cover map of each period was acquired from supervised classification using maximum likelihood algorithm in ArcGIS, then the land cover change was analysed through post-classification change detection of GIS-based method. . Accuracy assessment of classified images shows the overall accuracy is estimated as 88.71%, 85.81% and 91.61%, and overall Kappa coeffient statistical values of 0.87, 0.83 and 0.90 for the year 1998, 2008 and 2018 respectively. This study found that there was significant land cover change in the coastal areas of Kendari Bay. It was dominated by the expansion of built-up areas and bareland by 55% and 469.77% respectively, which was gained from the conversion of vegetation and wetland. Meanwhile, considerable reduction were shown in mangrove, wetland, forest and vegetation which have declined by 48.65%, 43.39%, 38.72% and 27.20%. Analysing land cover change is an effective way to understand the dynamics of land cover in coastal areas, and can be used for future land use planning and policies.

scholarly journals Land Cover Mapping Using Sentinel-1 SAR Satellite Imagery of Lagos State for 2017

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1399 ◽  
Author(s):  
Esther Makinde ◽  
Oluwaseun Oyelade
Keyword(s):  
Land Cover ◽  
Cloud Cover ◽  
Accuracy Assessment ◽  
Land Cover Mapping ◽  
Tropical Regions ◽  
Lagos State ◽  
Kappa Value ◽  
Land Cover Map ◽  
Sar Imagery ◽  
Wide Swath

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos state. In this work, a land cover map for Lagos state is created using Sentinel-1 Synthetic Aperture Radar (SAR) imagery. To this aim, a sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 over Lagos state, Nigeria was acquired and used. Results include an RGB composite of the image, classified image, with overall accuracy calculated as 0.757, while the kappa value for this project was evaluated to be about 0.719. The classification therefore passed the accuracy assessment. It is concluded that the Sentinel 1 SAR results has been effectively exploited for producing acceptably accurate land cover map of Lagos state, with relevant advantages for areas with cloud cover.

scholarly journals AN ASSESSMENT OF CITIZEN CONTRIBUTED GROUND REFERENCE DATA FOR LAND COVER MAP ACCURACY ASSESSMENT

2015 ◽  
Vol II-3/W5 ◽  
pp. 219-225 ◽  
Author(s):  
G. M. Foody
Keyword(s):  
Best Practices ◽  
Land Cover ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Good Practice ◽  
Thematic Mapping ◽  
Gold Standard Reference ◽  
Land Cover Map ◽  
Map Accuracy Assessment ◽  
Map Accuracy

It is now widely accepted that an accuracy assessment should be part of a thematic mapping programme. Authoritative good or best practices for accuracy assessment have been defined but are often impractical to implement. Key reasons for this situation are linked to the ground reference data used in the accuracy assessment. Typically, it is a challenge to acquire a large sample of high quality reference cases in accordance to desired sampling designs specified as conforming to good practice and the data collected are normally to some degree imperfect limiting their value to an accuracy assessment which implicitly assumes the use of a gold standard reference. Citizen sensors have great potential to aid aspects of accuracy assessment. In particular, they may be able to act as a source of ground reference data that may, for example, reduce sample size problems but concerns with data quality remain. The relative strengths and limitations of citizen contributed data for accuracy assessment are reviewed in the context of the authoritative good practices defined for studies of land cover by remote sensing. The article will highlight some of the ways that citizen contributed data have been used in accuracy assessment as well as some of the problems that require further attention, and indicate some of the potential ways forward in the future.

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