RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Abstract

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

“…It plays an important role in the global biogeochemical and energy cycles (Hao et al, 2016). In terms of environmental protection, vegetation can prevent wind and sand erosion, slow soil erosion, and effectively prevent desertification (Salomao, Silva & Machado, 2019;Zhang et al, 2019a;Zhang et al, 2019b). In terms of the energy cycle, vegetation can effectively reduce the concentration of greenhouse gases, facilitate global carbon balance, and maintain the global climate stability (Ndehedehe & Ferreira, 2019;Servera Vives et al, 2014).…”

“…It is important to monitor the long-term growth and change of vegetation and to explore its relationship with climate change in the study of global change (Mancino et al, 2013;Shao & Zhang, 2016). In particular, vegetation dynamics are very sensitive to climate change in arid and semi-arid areas (Zhang et al, 2019a;Zhang et al, 2019b;Shao et al, 2019a). Using remote sensing data to dynamically monitor the interannual variation of long-term sequence vegetation has become a prevailing research topic (Ernst-Brock et al, 2019;Teh et al, 2015).…”

Spatio-temporal fusion for remote sensing data: an overview and new benchmark

“…The analysis of multi-temporal image series is necessary and important in many remote sensing applications, such as vegetation/crop monitoring and estimation [1][2][3][4][5][6][7], evapotranspiration estimation [8], atmosphere monitoring [9], land-cover/land-use change detection [10], surface dynamic mapping [11], ecosystem monitoring [12], soil water content analysis [13], and detailed analysis of human-nature interactions [14]. The changes in the surface of the Earth usually occur within short time, and require high spatial resolution in order to be properly modeled.…”

RETRACTED: Zhang et al. Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China. Remote Sensing. 2019, 11, 133

“…We have been made aware that the experimental data, methodological framework, and description of the corresponding sections in this article are similar to those of another publication (in Chinese with an English abstract) by the first author himself, Zhang and Zeng [1], and Dr. Zeng was not aware of this article [2] submitted to Remote Sensing.…”

“…MDPI is a member of the Committee on Publication Ethics and takes its responsibility to enforce strict ethical policies and standards very seriously. To ensure the addition of only high-quality scientific works to the field of scholarly publication, the present paper [2] is retracted. The article is retracted with the agreement of all authors.…”