scholarly journals Photosynthesis, carbon acquisition and primary productivity of phytoplankton: a review dedicated to Colin Reynolds

Hydrobiologia ◽  
2020 ◽  
Vol 848 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Martin T. Dokulil ◽  
Kuimei Qian
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
Primary Productivity ◽  
Light Harvesting ◽  
Algal Growth ◽  
Long Time Series ◽  
Methods And Techniques ◽  
Long Time ◽  
The Subject

AbstractThe review intends to give an overview on developments, success, results of photosynthetic research and on primary productivity of algae both freshwater and marine with emphasis on more recent discoveries. Methods and techniques are briefly outlined focusing on latest improvements. Light harvesting and carbon acquisition are evaluated as a basis of regional and global primary productivity and algal growth. Thereafter, long-time series, remote sensing and river production are exemplified and linked to the potential effects of climate change. Lastly, the synthesis seeks to put the life achievements of Colin S. Reynolds into context of the subject review.

scholarly journals A Long Time-Series Radiometric Normalization Method for Landsat Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4505 ◽  
Author(s):  
Wei Wu ◽  
Xia Sun ◽  
Xianwei Wang ◽  
Jing Fan ◽  
Jiancheng Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Traditional Method ◽  
Acquisition Time ◽  
Reference Image ◽  
Optimization Strategy ◽  
Remote Sensing Images ◽  
Gray Scale ◽  
Long Time Series ◽  
Long Time

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)—radiometric characteristics of which do not change with time—are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.

Long time series of remote sensing to monitor the transformation research of Kubuqi Desert in China

2020 ◽  
Vol 13 (3) ◽  
pp. 795-809
Author(s):  
Xi Dong ◽  
Zhibo Chen ◽  
Mingquan Wu ◽  
Chunming Hu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Long Time Series ◽  
Long Time

scholarly journals Analysis of the Spatial and Temporal Pattern of Changes in Abandoned Farmland Based on Long Time Series of Remote Sensing Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2549
Author(s):  
Zhonghui Wei ◽  
Xiaohe Gu ◽  
Qian Sun ◽  
Xueqian Hu ◽  
Yunbing Gao
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Average Rate ◽  
Landsat 8 ◽  
Cultivated Land ◽  
Farmland Abandonment ◽  
Abandoned Farmland ◽  
Long Time Series ◽  
Long Time

With the rapid increase in the costs of rural labour and the adjustment of planting structures, the phenomenon of farmland abandonment has appeared in China. It is of great significance to promptly and accurately grasp the information on dynamic temporal and spatial changes in abandoned farmland to ensure national food security and the sustainable use of cultivated land. Luquan District in Hebei, China was selected as the research area based on multispectral images from Sentinel-2A, Landsat-7, and Landsat-8 combined with methods of random forest (RF) classification and vegetation index change detection. Rules for the identification of abandoned farmland were also developed, and remote sensing monitoring of the abandonment status of the cultivated land was also carried out in the study area. We also obtained the spatial distribution of abandoned and reclaimed farmland and analysed the frequency of farmland abandonment. The results show that the overall accuracy of the land-use time-series map ranged from 90.20% to 96.92% for the study period of 2010–2020. The average rate of farmland abandonment in the study area was 10.62%, with the lowest rate (5.83%) in 2020 and the highest (14.09%) in 2012. From 2011 to 2020, the maximum farmland abandonment area was 3906.02 hm2, and the minimum area was 1618.74 hm2. The farmland abandonment area showed a trend of first increasing and then decreasing. From 2012 to 2020, the maximum area of reclaimed farmland was 291.49 hm2, and the highest rate of reclamation was 14.26%. The overall reclamation rate was low. The abandonment frequency of most of the abandoned farmland was 1–3 years, covering an area of 8193.73 hm2, which comprised 79% of the total area of abandoned farmland. The frequency of abandonment was inversely proportional to the area of abandoned farmland. Farmland abandonment mainly occurred in hilly areas. We expect that our results can provide case studies for long time series in farmland abandonment research and can provide a reference for studying the driving factors, risk assessment, and policymaking with respect to abandoned farmland.

scholarly journals Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning

2019 ◽  
Vol 11 (14) ◽  
pp. 1639 ◽  
Author(s):  
Haoyu Wang ◽  
Xiang Zhao ◽  
Xin Zhang ◽  
Donghai Wu ◽  
Xiaozheng Du
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Classification ◽  
Series Data ◽  
Long Time Series ◽  
Quantitative Remote Sensing ◽  
Long Time ◽  
Year 2000

Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982–2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.

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