scholarly journals Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation

2017 ◽  
Vol 9 (10) ◽  
pp. 993 ◽  
Author(s):  
Mailys Lopes ◽  
Mathieu Fauvel ◽  
Annie Ouin ◽  
Stéphane Girard
Keyword(s):  
Time Series ◽  
Species Diversity ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Satellite Image ◽  
Temporal Heterogeneity ◽  
Grassland Species

scholarly journals Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1047 ◽  
Author(s):  
Ying Sun ◽  
Jianfeng Huang ◽  
Zurui Ao ◽  
Dazhao Lao ◽  
Qinchuan Xin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Species Diversity ◽  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Diversity Index ◽  
Tree Species Diversity ◽  
Individual Tree ◽  
Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

scholarly journals Comparision of observed and theoretical amplitudes of oscillations above granules and intergranular lanes

2001 ◽  
Vol 203 ◽  
pp. 192-194
Author(s):  
E. V. Khomenko
Keyword(s):  
Time Series ◽  
Wave Propagation ◽  
Spatial Resolution ◽  
3D Model ◽  
High Spatial Resolution ◽  
Model Atmosphere ◽  
Solar Photosphere ◽  
Line Profiles ◽  
Physical Conditions ◽  
Excitation Mechanisms

We do modeling of the wave propagation in the solar photosphere. NLTE synthesis of the time series of the Fe I 5324 Å line profiles is performed using 3D model atmosphere. Velocity and intensity oscillations resulted from computations are compared with high spatial resolution observations. We conclulde that differences in oscillatory amplitudes above granules and intergranular lanes can be produced by variations of the physical conditions in these structures without invoking any excitation mechanisms.

scholarly journals Estimation of grassland use intensities based on high spatial resolution LAI time series

2015 ◽  
Vol XL-7/W3 ◽  
pp. 285-291 ◽  
Author(s):  
S. Asam ◽  
D. Klein ◽  
S. Dech
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Management Practices ◽  
High Spatial Resolution ◽  
Vegetation Period ◽  
Grassland Management ◽  
Transfer Model ◽  
Land Use Patterns ◽  
Area Index ◽  
Intensively Managed

The identification and surveillance of agricultural management and the measurement of biophysical canopy parameters in grasslands is relevant for environmental protection as well as for political and economic reasons, as proper grassland management is partly subsidized. An ideal monitoring tool is remote sensing due to its area wide continuous observations. However, due to small-scaled land use patterns in many parts of central Europe, a high spatial resolution is needed. In this study, the feasibility of RapidEye data to derive leaf area index (LAI) time series and to relate them to grassland management practices is assessed. The study area is the catchment of river Ammer in southern Bavaria, where agricultural areas are mainly grasslands. While extensively managed grasslands are maintained with one to two harvests per year and no or little fertilization, intensive cultivation practices compass three to five harvests per year and turnover pasturing. <br><br> Based on a RapidEye time series from 2011 with spatial resolution of 6.5 meters, LAI is derived using the inverted radiation transfer model PROSAIL. The LAI in this area ranges from 1.5 to 7.5 over the vegetation period and is estimated with an RMSE between 0.7 and 1.1. The derived LAI maps cover 85 % of the study area’s grasslands at least seven times. Using statistical metrics of the LAI time series, different grassland management types can be identified: very intensively managed meadows, intensively managed meadows, intensively managed pastures, and extensively managed meadows and moor. However, a precise identification of the mowing dates highly depends on the coincidence with satellite data acquisitions. Further analysis should focus therefor on the selection of the temporal resolution of the time series as well as on the performance of further vegetation parameters and indices compared to LAI.

scholarly journals Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850–2100)

2021 ◽  
Author(s):  
Xikun Wei ◽  
Guojie Wang ◽  
Donghan Feng ◽  
Zheng Duan ◽  
Daniel Fiifi Tawia Hagan ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Resolution ◽  
Bias Correction ◽  
High Spatial Resolution ◽  
Super Resolution ◽  
Future Climate Change ◽  
Observation Data ◽  
Near Surface ◽  
Spatial Downscaling

Abstract. Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850–1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a ‘tertiary class echelon’ condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021).

scholarly journals A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 817-820
Author(s):  
B. Liu ◽  
J. Chen ◽  
H. Xing ◽  
H. Wu ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Image Data ◽  
Resolution Time ◽  
Simulated Experiment ◽  
Land Cover Data ◽  
Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

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