scholarly journals First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 117
Author(s):  
Céline Bassine ◽  
Julien Radoux ◽  
Benjamin Beaumont ◽  
Taïs Grippa ◽  
Moritz Lennert ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Use Planning ◽  
Climate Modeling ◽  
Decision Rules ◽  
Ancillary Data ◽  
Map Labeling ◽  
Small Water ◽  
Very High Spatial Resolution ◽  
Land Cover Map ◽  
Land Cover Maps

Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.

scholarly journals From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

2021 ◽  
Vol 13 (9) ◽  
pp. 1700
Author(s):  
Dang Hung Bui ◽  
László Mucsi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Decision Rules ◽  
Random Forest Classifier ◽  
Landsat Images ◽  
Object Based ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Land Cover Maps

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

scholarly journals Key Challenges for Land Use Planning and Its Environmental Assessments in the Abuja City-Region, Nigeria

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 443
Author(s):  
Evidence Chinedu Enoguanbhor ◽  
Florian Gollnow ◽  
Blake Byron Walker ◽  
Jonas Ostergaard Nielsen ◽  
Tobia Lakes
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Assessment ◽  
Land Use Planning ◽  
Environmental Sustainability ◽  
Sub Saharan Africa ◽  
Environmental Assessments ◽  
City Regions ◽  
Sub Saharan ◽  
Land Cover Maps

Land use planning as strategic instruments to guide urban dynamics faces particular challenges in the Global South, including Sub-Saharan Africa, where urgent interventions are required to improve urban and environmental sustainability. This study investigated and identified key challenges of land use planning and its environmental assessments to improve the urban and environmental sustainability of city-regions. In doing so, we combined expert interviews and questionnaires with spatial analyses of urban and regional land use plans, as well as current and future urban land cover maps derived from Geographic Information Systems and remote sensing. By overlaying and contrasting land use plans and land cover maps, we investigated spatial inconsistencies between urban and regional plans and the associated urban land dynamics and used expert surveys to identify the causes of such inconsistencies. We furthermore identified and interrogated key challenges facing land use planning, including its environmental assessment procedures, and explored means for overcoming these barriers to rapid, yet environmentally sound urban growth. The results illuminated multiple inconsistencies (e.g., spatial conflicts) between urban and regional plans, most prominently stemming from conflicts in administrative boundaries and a lack of interdepartmental coordination. Key findings identified a lack of Strategic Environmental Assessment and inadequate implementation of land use plans caused by e.g., insufficient funding, lack of political will, political interference, corruption as challenges facing land use planning strategies for urban and environmental sustainability. The baseline information provided in this study is crucial to improve strategic planning and urban/environmental sustainability of city-regions in Sub-Saharan Africa and across the Global South, where land use planning faces similar challenges to address haphazard urban expansion patterns.

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.

scholarly journals Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia

2019 ◽  
Vol 41 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Megersa Olumana Dinka ◽  
Degefa Dhuga Chaka
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Landsat Imagery ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Post Processing ◽  
Ancillary Data ◽  
The Stability ◽  
Land Cover Maps

Abstract Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23 years, the aerial coverage of forest and grass lands declined by 8.5% and 4.3%, respectively. On the other hand, agricultural and shrub lands expanded by 9.1% and 3.7%, respectively. This shows that most of the previously covered by forest and grass lands are mostly shifted to the rapidly expanding farm land use classes. The findings of this study suggested that the rate of LULCC over the study period, particularly deforestation due to the expansion of farmland need to be given due attention to maintain the stability and sustainability of the ecosystem.

scholarly journals JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

2018 ◽  
Vol 10 (9) ◽  
pp. 1406 ◽  
Author(s):  
Phan Duong ◽  
Ta Trung ◽  
Kenlo Nasahara ◽  
Takeo Tadono
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Near Infrared ◽  
Climate Modeling ◽  
Rice Paddy ◽  
Shuttle Radar Topography Mission ◽  
Ancillary Data ◽  
Data Accessibility ◽  
Central Vietnam ◽  
Bare Land

Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

scholarly journals Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels

2020 ◽  
Vol 12 (3) ◽  
pp. 503
Author(s):  
Li ◽  
Chen ◽  
Foody ◽  
Wang ◽  
Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Frequency ◽  
Land Cover Mapping ◽  
Remote Sensing Images ◽  
Ancillary Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Land Cover Maps

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

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>

Sensitivity of the ECMWF Land surface model to vegetation and LU/LC maps

2020 ◽  
Author(s):  
Souhail Boussetta ◽  
Gianpaolo Balsamo ◽  
Emanuel Arduini ◽  
Miguel Nogueira ◽  
Gabriele Arduini ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Land Use Land Cover ◽  
Area Index ◽  
Land Cover Map ◽  
Land Cover Maps

&lt;p&gt;&lt;span&gt;&lt;span&gt;The effects of vegetation and land use/land cover maps on surface energy and carbon fluxes predictions from land surface model are investigated. The model is applied at global scale and a comparison between two configurations using different land cover maps is performed. In the first configuration, the land cover is based on the operational GLCCv1.2 map, in the second the ESA-CCI land cover map is used.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;span&gt;Based on these two configurations, the observation operator that disaggregates the satellite-based leaf area index into high and low vegetation components is also modified to ensure optimal conservation of the observed LAI. The Seasonal variability of the vegetation cover is also investigated by introducing a modified lamber-beer formulation that allows varying the vegetation cover as a function of the LAI. &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

scholarly journals Reconstruction of Residential Land Cover and Spatial Analysis of Population in Bursa Region (Turkey) in the Mid-Nineteenth Century

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1077
Author(s):  
Eda Ustaoglu ◽  
Mustafa Erdem Kabadayı
Keyword(s):  
Land Cover ◽  
Regression Models ◽  
Census Data ◽  
Ordinary Least Squares ◽  
Historical Study ◽  
Ancillary Data ◽  
Residential Land ◽  
Human Environment ◽  
Natural Amenities ◽  
Land Cover Maps

The historic reconstruction of residential land cover is of significance to uncover the human-environment relationship and its changing dynamics. Taking into account the historical census data and cadastral maps of seven villages, this study generated residential land cover maps for the Bursa Region in the 1850s using a model based on natural constraints, land zoning, socio-economic factors and residential suitability. Two different historical reconstructions were generated; one based on a high density residential model and another based on a low density model. The simulated landcover information was used as an ancillary data to redistribute aggregated census counts to fine scale raster cells. Two different statistical models were developed; one based on probability maps and the other applying regression models including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The regression models were validated with historical census data of the 1840s. From regression models, socio-economic and physical characteristics, accessibility and natural amenities showed significant impacts on the distribution of population. Model validation analysis revealed that GWR is more accurate than OLS models. The generated residential land cover and gridded population datasets can provide a basis for the historical study of population and land use.

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.

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