Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop 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.

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

Remote Sensing ◽

10.3390/rs13122301 ◽

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.

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

Journal of Hydrology Regional Studies ◽

10.1016/j.ejrh.2021.100837 ◽

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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Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map

Remote Sensing ◽

10.3390/rs12091418 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1418

Author(s):

Runmin Dong ◽

Cong Li ◽

Haohuan Fu ◽

Jie Wang ◽

Weijia Li ◽

...

Keyword(s):

Land Cover ◽

Training Data ◽

Training Dataset ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Large Area ◽

National Scale ◽

Substantial Progress ◽

Efficient Learning ◽

Land Cover Maps

Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.

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