Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh

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.

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

Remote Sensing ◽

10.3390/rs10081212 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1212 ◽

Cited By ~ 4

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.

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

10.5194/egusphere-egu2020-3814 ◽

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

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.&#160;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.&#160;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.

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

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

10.5194/isprsarchives-xl-1-245-2014 ◽

2014 ◽

Vol XL-1 ◽

pp. 245-252 ◽

Cited By ~ 1

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. 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 (Melanerpes lewis) and the White-tailed Jack Rabbit (Lepus townsendii) and Brown Headed nuthatch (Sitta pusilla). 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 −14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit (Lepus townsendii) had a net change of −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.

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

The Forestry Chronicle ◽

10.5558/tfc791075-6 ◽

2003 ◽

Vol 79 (6) ◽

pp. 1075-1083 ◽

Cited By ~ 109

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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Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

Remote Sensing ◽

10.3390/rs13081596 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1596

Author(s):

Bo Zhong ◽

Aixia Yang ◽

Kunsheng Jue ◽

Junjun Wu

Keyword(s):

Time Series ◽

Land Cover ◽

Regional Scale ◽

Heihe River ◽

Land Cover Mapping ◽

High Quality ◽

Long Time Series ◽

Training Samples ◽

Long Time ◽

Land Cover Map

Long time series of land cover changes (LCCs) are critical in the analysis of long-term climate, environmental, and ecological changes. Although several moderate to fine resolution global land cover datasets have been publicly released and they show strong consistency at the global scale, they have large deviations at the regional scale; furthermore, high-quality land cover datasets from before 2000 are not available and the classification consistency among different datasets is not very good. Thus, long time series of land cover datasets with high quality and consistency are in great demand but they are still unavailable, even at the regional scale. The Landsat series of satellite imagery composed of eight successive satellites can be traced back to 1972 and it is, therefore, possible to produce a long time series land cover dataset. In addition, the newly available satellite data have the capability to construct time series satellite images and a time series analysis method such as LCMM can be employed for making high-quality land cover datasets. Therefore, by taking the advantages of the two categories of satellite data, we proposed a new time series land cover mapping method based on machine learning and it, thereafter, is applied to Heihe River Basin (HRB) for verification purposes. Firstly, the high-quality land cover datasets at HRB from 2011–2015, which were retrieved using the LCMM method, are used for quickly and accurately making training samples. Secondly, a strategy for transferring the training samples after 2011 to earlier years is established. Thirdly, the random forest model is employed to train the selected yearly samples and a land cover map for every year is subsequently made. Finally, comprehensive analysis and validation are carried out for evaluation. In this study, a long time series land cover dataset including 1986, 1990, 1995, 2000, 2005, 2010, 2011, 2012, 2013, 2014, and 2015 is finally made and an average precision of about 90% is achieved. It is the longest time series land cover map with 30 m resolution at HRB and the dataset has good time continuity and stability.

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Resolution Enhancement for Large-Scale Land Cover Mapping via Weakly Supervised Deep Learning

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.6.405 ◽

2021 ◽

Vol 87 (6) ◽

pp. 405-412

Author(s):

Qiutong Yu ◽

Wei Liu ◽

Wesley Nunes Gonçalves ◽

José Marcato Junior ◽

Jonathan Li

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Cover ◽

Spatial Resolution ◽

Satellite Images ◽

Land Cover Mapping ◽

Fusion Method ◽

The Public ◽

Land Cover Map ◽

Weakly Supervised

Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS.

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Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

Remote Sensing ◽

10.3390/rs11091056 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1056 ◽

Cited By ~ 7

Author(s):

Xiao Zhang ◽

Liangyun Liu ◽

Xidong Chen ◽

Shuai Xie ◽

Yuan Gao

Keyword(s):

Land Cover ◽

Spatial Resolution ◽

Earth Science ◽

Landsat Imagery ◽

European Space Agency ◽

Accurate Method ◽

Land Cover Mapping ◽

Land Cover Types ◽

Multi Temporal ◽

Land Cover Map

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

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Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Remote Sensing ◽

10.3390/rs12040602 ◽

2020 ◽

Vol 12 (4) ◽

pp. 602 ◽

Cited By ~ 10

Author(s):

Qingyu Li ◽

Chunping Qiu ◽

Lei Ma ◽

Michael Schmitt ◽

Xiao Zhu

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Google Earth ◽

Land Cover Mapping ◽

Landsat 8 ◽

Climate Zone ◽

Analysis Experiment ◽

Natural Land ◽

Land Cover Map ◽

Google Earth Engine

The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

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

Proceedings ◽

10.3390/proceedings2221399 ◽

2018 ◽

Vol 2 (22) ◽

pp. 1399 ◽

Cited By ~ 3

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.

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