Abstract:Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The "woodland" LAI/FPAR is the worst, with a spatial OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2520 similarity (O) of 58.17% due to the misclassification between "woodland" and "others". The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.
Dynamic monitoring of vegetation coverage changes, especially on a relatively large temporal scale, have important practical significance in urban planning and environmental protection. The objective of this study is to dynamically investigate the urban landscape patterns of vegetation coverage based on remote sensing techniques. Multi-temporal Landsat images of 1990, 2000 and 2013 were firstly used to produce three vegetation coverage maps of Hefei City, Anhui Province, China with five grades using the NDVI (Normalized Difference Vegetation Index) dimidiate pixel model. Subsequently, a total of eight landscape pattern indictors in FRAGSTATS 4.2 were selected to analyze the dynamic characteristics of area, quantity and density for the study area with different vegetation coverage grades. The results showed that 1) the dominant vegetation coverage of 1990, 2000 and 2013 were the high vegetation coverage, the moderate vegetation coverage and the moderate-to-high vegetation coverage, respectively. The acreage of non-vegetation coverage increased by 1.89%, while the high vegetation coverage decreased by 10.48% from 1990 to 2013; 2) the quantity and density of patches decreased by 33.42% and 33.41% during 1990-2013. Shannon's diversity index and Shannon's evenness index increased from 0.92 in 1990 to 0.97 in 2000, and then declined to 0.96 in 2013; and 3) the contagion index had an upward trend and conversely the aggregation index showed no significant changes, but both of them were close to 1 during 1990-2013. In comparison with natural influences, the primary driving forces causing the changes were ascribed to human factors including the rapid population growth and fast-growing urban areas.
FRC technology mainly uses the visual inertia of human eyes which can be regarded as persistence of vision phenomenon by sequential frames. It is especially important to make the 6 bit panel display abundant colors. The paper has deeply researched some possible stripes of the FRC algorithm caused by fixed pattern. A novel stripe removable algorithm has been brought up. It can effectively avoid the horizontal, vertical or diagonal stripe by displaying different temporal and spatial patterns. Various experiments show that the novel algorithm can display high resolution image in the lowly variety devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.