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 Land Use / Land Cover Mapping of Nahra Nala Watershed Using Sentinel-2B Imagery

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Deepak Patle
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Accuracy Assessment ◽  
Geographical Area ◽  
Ground Truth ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Kappa Value

Land is a limited natural resource which restricts no further increase in a cultivated area. Moreover, due to the increasing population, the pressure on this resource is increasing day by day. Hence, land use/land cover (LU/LC) information is very much necessary for the best possible use by maximizing outputs sustainably from this diminishing resource such that good planning and management can be done to meet the demand of the ever-increasing population. Therefore, a study has been conducted for land use/land cover mapping using SENTINEL-2B satellite data having a fine spatial resolution of Nahra nala watershed, which is a tributary of Wainganga river situated in Balaghat district of Madhya Pradesh, India. Five land use/land cover classes were identified, namely water bodies, agricultural land, forest, habitation (built-up), and wasteland in the study area. The study area possesses forest as the predominant LU/LC class with 83.79 percent of the total geographical area of the watershed. Accuracy assessment was also applied to the final classified results based on the ground truth points or known reference pixels along with Google Earth imageries. The overall classification accuracy of 95.52% with the kappa value of 0.92 was achieved.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

scholarly journals Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

2018 ◽  
Vol 10 (8) ◽  
pp. 1212 ◽  
Author(s):  
Xiaohong Yang ◽  
Zhong Xie ◽  
Feng Ling ◽  
Xiaodong Li ◽  
Yihang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Fuzzy C Means Clustering ◽  
Land Cover Map ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017

2019 ◽  
Vol 27 (1) ◽  
pp. 66-74 ◽  
Author(s):  
Esther Oluwafunmilayo Makinde ◽  
Edward Oluwasegun Oyelade
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
Lagos State

How to improve 3-m resolution land cover mapping from imperfect 10-m resolution land cover mapping product?

2020 ◽  
Author(s):  
Runmin Dong ◽  
Haohuan Fu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Histogram Equalization ◽  
Land Cover Mapping ◽  
Data Set ◽  
Chesapeake Bay Watershed ◽  
Land Cover Map ◽  
Previous State ◽  
Human Interpretation ◽  
Large Scale Land

<p>Land cover mapping has made drastic progress with the improvement of the resolution of remote sensing images in recent research. However, with various limitations of public land cover datasets, human efforts on interpreting and labelling images still account for a significant part of the total cost. For example, it took 10 months and $1.3 million to label about 160,000 square kilometers in the Chesapeake Bay watershed in the northeastern United States. Therefore, it is significant to consider the human interpreting cost of the large-scale land cover mapping.</p><p> </p><p>In this work, we explore a possible solution to achieve 3-m resolution land cover mapping without any human interpretation. This is made possible thanks to a 10-m resolution global land cover map developed for the year of 2017. We propose a complete workflow and a novel deep learning based network to transform the imperfect 10-m resolution land cover map to a preferable 3-m resolution land cover map, which is beneficial to reduce the research thresholds in this community and give similar studies as an example. As we use the imperfect training label, a well-designed and robust approach is strongly needed. We integrate a deep high-resolution network with instance normalization, adaptive histogram equalization, and a pruning process for large-scale land cover mapping.</p><p> </p><p>Our proposed approach achieves the overall accuracy (OA) of 86.83% on the test data set for China, improving the previous state-of-the-art accuracies of 10-m resolution land cover mapping product by 5.35% in OA. Moreover, we present detailed results obtained over three mega cities in China as example and demonstrate the effectiveness of our proposed approach for 3-m resolution large-scale land cover mapping.</p>

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 Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program

2003 ◽  
Vol 79 (6) ◽  
pp. 1075-1083 ◽  
Author(s):  
M A Wulder ◽  
J A Dechka ◽  
M A Gillis ◽  
J E Luther ◽  
R J Hall ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Storage ◽  
Land Cover Mapping ◽  
Landsat Data ◽  
Large Area ◽  
Joint Project ◽  
Complete Representation ◽  
Forested Area ◽  
Area Of Interest ◽  
Land Cover Map

A priority of the Canadian Forest Service and Canadian Space Agency joint project, Earth Observation for Sustainable Development of Forests (EOSD), is the production of a land cover map of the forested area of Canada based upon Landsat data. The land cover will be produced through a partnership of federal, provincial and territorial governments, universities, and industry. The short-term goal of EOSD is to complete a land cover map representing year 2000 forested area conditions by early 2006. Over the longer term, EOSD will aim to produce land cover products to capture changes in forest conditions over time to support national and international reporting requirements. The forested area of Canada represents approximately half of Canada's landmass, requiring over 450 scenes for complete coverage (with overlap minimized). EOSD is working with provincial and territorial mapping agencies that have on-going land cover mapping programs to optimize production capacity. It is envisioned that the combined output of EOSD and provincial and territorial land cover mapping programs will be integrated with maps developed by other sectors and agencies (such as agriculture) to produce a complete representation of the land cover of Canada. Large-area land cover mapping using remote sensing is a relatively new phenomenon. Advances in data storage capabilities, computing power, and increases in the affordability of data have allowed for large-area projects to be undertaken in ways previously not possible. The manner in which a large-area mapping project is approached is related to a number of factors including the spatial extent of the area of interest, the spatial resolution of the selected sensor, and the products which are to be generated. In this communication we report on the strategy, methods, and status of the EOSD land cover mapping program of the forested area of Canada. Key words: Canada, land cover, forest inventory, EOSD, Landsat, unsupervised classification, NFI

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