A non-parametric classification system for a partially unsupervised updating of land-cover maps

2002 ◽  
Author(s):  
L. Bruzzone ◽  
R. Cossu
Keyword(s):  
Land Cover ◽  
Classification System ◽  
Parametric Classification ◽  
Land Cover Maps ◽  
Non Parametric

scholarly journals CLASSIFICATION SYSTEM DRIVES DISAGREEMENT AMONG BRAZILIAN VEGETATION MAPS AT A SAMPLE AREA OF THE SEMIARID CAATINGA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 201-206
Author(s):  
E. Bontempo ◽  
M. C. Demirel ◽  
C. Corsini ◽  
F. Martins ◽  
D. Valeriano
Keyword(s):  
Land Cover ◽  
Classification System ◽  
Sustainable Use ◽  
Classification Systems ◽  
Northeastern Brazil ◽  
Semiarid Region ◽  
Human Occupation ◽  
Spatial Efficiency ◽  
Sample Area ◽  
Land Cover Maps

Abstract. The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA’s GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA’s MODIS MCD12Q1. This was done on a sample area where many of the Caatinga’s vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns. Our results show considerable disagreement between the maps tested and their class semantics, with IBGE’s and ProBio’s being the most similar among all national maps and MapBiomas’ the most closely related to global LC maps. The nature of the observed disagreement between these maps shows they diverge not only in the application of their classification systems, but also in their mapped spatial pattern, signaling the need for a better classification system and a better map of vegetation and land cover for the region.

Incorporating the uncertainty of linguistic-scale reference data to assess accuracy of land-cover maps using fuzzy intervals

2013 ◽  
Vol 34 (11) ◽  
pp. 4008-4024 ◽  
Author(s):  
Pedro Sarmento ◽  
Cidália C. Fonte ◽  
Mário Caetano ◽  
Stephen V. Stehman
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Land Cover Maps

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 Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

scholarly journals Improvement of the ESA CCI Land cover maps for water balance analysis in tropical regions: A case study in the Muda River Basin, Malaysia

2021 ◽  
Vol 36 ◽  
pp. 100837
Author(s):  
Mou Leong Tan ◽  
Yi Lin Tew ◽  
Kwok Pan Chun ◽  
Narimah Samat ◽  
Shazlyn Milleana Shaharudin ◽  
...  
Keyword(s):  
Land Cover ◽  
Water Balance ◽  
River Basin ◽  
Tropical Regions ◽  
Balance Analysis ◽  
Land Cover Maps
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