Object Oriented Land Cover Mapping of the Kanchenjunga Conservation Area (KCA) in Nepal, for Sustainable Development and Use of Natural Resources

2006 ◽  
Author(s):  
T. Kellenberger ◽  
W. Schubiger ◽  
K. Itten
Keyword(s):  
Sustainable Development ◽  
Land Cover ◽  
Natural Resources ◽  
Object Oriented ◽  
Land Cover Mapping ◽  
Conservation Area ◽  
Kanchenjunga Conservation Area

Object‐oriented classification for urban land cover mapping with ASTER imagery

2007 ◽  
Vol 28 (20) ◽  
pp. 4645-4651 ◽  
Author(s):  
Y. Chen ◽  
P. Shi ◽  
T. Fung ◽  
J. Wang ◽  
X. Li
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Urban Land Cover

scholarly journals OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 987 ◽  
Author(s):  
Guangbin Lei ◽  
Ainong Li ◽  
Jinhu Bian ◽  
He Yan ◽  
Lulu Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Classifier Ensemble ◽  
Land Cover Mapping ◽  
Initial Training ◽  
Sample Collection ◽  
Training Samples ◽  
Multiple Classifier ◽  
Practical Object

Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%–8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.

scholarly journals Mapping Urban and Peri-Urban Land Cover in Zimbabwe: Challenges and Opportunities

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 114-147
Author(s):  
Courage Kamusoko ◽  
Olivia Wadzanai Kamusoko ◽  
Enos Chikati ◽  
Jonah Gamba
Keyword(s):  
Sustainable Development ◽  
Land Cover ◽  
United Nations ◽  
Urban Areas ◽  
Rainy Season ◽  
Dry Season ◽  
Land Cover Mapping ◽  
Urban Centers ◽  
Geospatial Information ◽  
Study Results

Accurate and current land cover information is required to develop strategies for sustainable development and to improve the quality of life in urban areas. This study presents an approach that combines multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data, and a random forest (RF) classifier in order to map land cover in four major urban centers in Zimbabwe. The specific objective of this study was to assess the potential of multi-seasonal (rainy, post-rainy, and dry season) S1, rainy season S2, post-rainy season, dry season S2, multi-seasonal S2, and multi-seasonal composite S1 and S2 data for mapping land cover in urban areas. The study results show that the combination of multi-seasonal S1 and S2 data improve land cover mapping in urban and peri-urban areas relative to only multi-seasonal S1, mono-seasonal S2, and multi-seasonal S2 data. The overall accuracy scores for the multi-seasonal S1 and S2 land cover maps are above 85% for all urban centers. Our results indicate that rainy and post-rainy S2 spectral bands, as well as dry-season S1 VV and VH bands (ascending orbit) are the most important features for land cover mapping. In particular, S1 data proved useful in separating built-up areas from cropland, which is usually problematic when only optical imagery is used in the study area. While there are notable improvements in land cover mapping, some challenges related to the S1 data analysis still remain. Nonetheless, our land cover mapping approach shows a potential to map land cover in other urban areas in Zimbabwe or in Sub-Sahara Africa. This is important given the urgent need for reliable geospatial information, which is required to implement the United Nations Sustainable Development Goals (UN SDGs) and United Nations New Urban Agenda (NUA) programmes.

Assessing the suitability of GlobeLand30 for land cover mapping and sustainable development in Malaysia using error matrix and unbiased area Estimation

2020 ◽  
pp. 1-21
Author(s):  
Abdul-Lateef Balogun ◽  
Syazmeer Arrafi Mohd Said ◽  
Abdulkadir Taofeeq Sholagberu ◽  
Yusuf A. Aina ◽  
Omar Faisal Althuwaynee ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Error Matrix ◽  
Area Estimation

scholarly journals Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

2020 ◽  
Vol 12 (23) ◽  
pp. 3862
Author(s):  
Joana Borges ◽  
Thomas P. Higginbottom ◽  
Elias Symeonakis ◽  
Martin Jones
Keyword(s):  
Ecosystem Services ◽  
Land Cover ◽  
Dry Season ◽  
Sensor Data ◽  
Land Cover Mapping ◽  
Heterogeneous Environments ◽  
Wet Season ◽  
Conservation Area ◽  
Free Data ◽  
Sentinel 2

Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.

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