Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Evaluation of rapid phase clock/bias products for PPP ambiguity resolution and its application to the M7.1 2019 Ridgecrest,California earthquake

Precise point positioning with ambiguity resolution (PPP-AR) is a useful tool for high-precision geodetic and geophysical applications, while phase bias products are the prerequisite to implement PPP-AR. Wuhan University has been providing the final (the best operationally post-processing solution based) phase clock/bias products with a latency of two weeks since March of 2019, while a dedicated open-source software package PRIDE PPP-AR is released to leverage these products for high-precision positioning. In order to satisfy some both time and precision critical applications, such as rapid earthquake response, Wuhan University also released rapid (with comparable quality but with much shorter delivery latency) phase clock/bias products with a latency of less than 24 h and updated PRIDE PPP-AR in July 2019. We first introduce the phase clock/bias generation and validation schemes and the maintenance of routine products provision. Then, with 14 days (July 2 to July 15 in 2019) of GPS data collected from 146 globally distributed IGS (International GNSS Service) stations, we evaluated the positioning performance of the rapid products with respect to their final counterparts. It is found that positioning precision of PPP-AR using rapid products is comparable to that using final products, especially in kinematic positioning mode. When rapid products are used, the RMS of PPP-AR in static mode with respect to IGS weekly solutions can reach 1.7 mm, 1.8 mm and 5.5 mm in the east, north and up components, respectively. Furthermore, the RMS of epoch-wise positions with respect to daily solutions for the east, north and up components are 0.51 cm, 0.57 cm and 1.51 cm for PPP-AR with rapid products in kinematic mode. It demonstrates that the rapid phase clock/bias products can sufficiently meet the precision requirement of most geodetic and geophysical applications yet with much shorter time delay. Finally, we study the July 6th M7.1 2019 Ridgecrest, California earthquake using the rapid phase clock/bias products and demonstrate their comparable performance against the final products.     

Using land use change trajectories to quantify the effects of urbanization on urban heat island

This paper proposed a quantitative method of land use change trajectory, which means the succession among different land use types across time, to examine the effects of urbanization on an urban heat island (UHI). To accomplish this, multi-temporal images from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) of Xiamen City in China from 1987 to 2007 were selected. First, the land use change trajectories were extracted based on the classified images from different years. Then the land surface temperatures (LST) were retrieved and the magnitudes of the UHI were evaluated using the UHI intensity (UHII) indicator. Finally, the indices of the contribution to UHI intensity (CUHII) were constructed and calculated to quantify the effects of each land use change trajectory on the UHI during urbanization. The results demonstrated that the land use change trajectories and CUHII are effective and useful in quantifying the effects of urbanization on UHI. In Xiamen City, a total of 2218 land use change trajectories were identified and 530 of them were the existing urban or urbanization trajectories. The UHII presents a trend of continuous increase from 0.83 °C in 1987 to 2.14 °C in 2007. With respect to the effects of urban growth on UHI, the contribution of existing urban area to UHI decreased during urbanization. Prior to 2007, the existing urban area of trajectory NO. 44444 had the most significant effect on UHI with the greatest CUHII, while the value has decreased from 55.00% in 1987 to 13.03% in 2007 because of the addition of new urbanized area. In 2007, the greatest CUHII was replaced by a trajectory from farmland to built-up area (NO. 22224) with the CUHII of 21.98%, followed by the existing urban area of trajectory NO. 44444 with the CUHII of 13.03%. These results provide not only a new methodology to assess the environmental effects of urbanization, but also decision-supports for the planning and management of cities.     

Influence of land surface parameters on the spatio-seasonal land surface temperature regime in rural West Bengal,India

The magnitude and causes of changes in the land surface temperature of rural areas have not been extensively studied. The thermal band of Landsat imagery is taken to extract winter, summer, and monsoon season land surface temperature (LST) and relate it to surface parameters over a 30-year period. From the extracted parameters constructed a prospective surface temperature (PST) model using Multivariate Adaptive Regression Splines. The Chandrabhaga river basin in West Bengal of the lateritic Rarh Tract at the Chota Nagpur Plateau fringe was chosen as the study area because it is far from urban influences, to avoid the well-known heat island effect. Over the study period, summer and winter average LST increased linearly by 0.085?°C/y and 0.016?°C/y respectively. These results were validated with air temperature (RMSE?=?x and y, respectively). Over time more of the area is in the higher temperature zones, e.g., in April 2011, 4% area exceeded >32°, whereas in 2015 this proportion reached 52%. PST models of all the seasons were moderate to highly correlate (0.57–0.87) with actual LST, showing the value of this model. It also revealed the relative importance of the regional factors. Based on this information factor management is a scientific step to restrict or minimize the temperature rise effect.     

Higher order ionospheric error correction in BDS precise orbit determination

The ionospheric error affects the accuracy of the Global Navigation Satellite Systems observation and precise orbit determination. Usually, only the first order ionospheric error is considered, which can be eliminated by the ionospheric-free linear combination observation. But the remaining higher order ionospheric error will affect the accuracy of observations and their applications. In this paper, the influence of the higher order ionospheric error have been studied by using the International Geomagnetic Reference Field 13 and the Global Ionosphere Maps model produced by the Center for Orbit Determination in Europe. Focus on ionospheric error, the experiment of paper at doy 302 in 2019, which show that the second order ionospheric error impacting BeiDou Navigation Satellite System (BDS) B1I and B3I observation is 6.3569 mm and 11.8484 mm, respectively. Whereas, the third order ionospheric error impacting BDS B1I and B3I observation is 0.1734 mm and 0.3977 mm, respectively. Due to the current measurement accuracy of BDS carrier-phase observation can reach 2 mm, the influence of high order ionospheric error on observation should be considered. For BDS precise orbit determination, the orbit overlapping results are indicated that its orbit accuracy can be improved approximately 5 mm with the higher order ionospheric error correction, which is also in agreement with the results of Satellite Laser Ranging in this work.     

Dynamics of total suspended solid concentrations in the lower Raidak river (Himalayan foreland Basin), India

Accurate assessment of total suspended solids (TSS) is one of the most important parameters for the management of health of aquatic ecosystem. Due to the limitation of traditional TSS measurement methods, recently developed remote sensing based algorithm (Wen algorithm) has been used to measure the TSS concentration in a Himalayan foothill river like Raidak. Additionally, to examine the spatiotemporal characteristics of total suspended solids (TSS), total three consecutive years (2019, 2020 and 2021) along with different seasons have been selected. The Nechad algorithm has been used to validate the recalibrated algorithm. The result also indicates that Wen algorithm is also highly consistent with the Nechad algorithm (R² value is greater than 0.90). It is also estimated that the concentration of TSS becomes very high particularly in monsoon months (242 mg/l, 270 mg/l and 246 mg/l for the year 2019, 2020 and 2021). The Raidak river catchment is located in the foothill zone of the lofty Himalayan range and the region is totally influenced by Indian summer monsoon. In the Raidak river course, the seasonal sediment flux is highly correlated with rainfall, stream flow, cross sectional area and high anthropogenic stress. The NDTI (Normalize Difference Turbidity Index) value (Turbidity) is greater in monsoon season than pre and post monsoonal periods (0.035, 0.0851 and 0.0201 for the years 2019, 2020 and 2021 respectively). ANOVA result shows a significant difference among the TSS values in different years (p=<0.05).     

Detection and monitoring of earthquake precursors: TwinSat,a Russia–UK satellite project

There is now a body of evidence to indicate that coupling occurs between the lithosphere–atmosphere–ionosphere prior to earthquake events. Nevertheless the physics of these phenomena and the possibilities of their use as part of an earthquake early warning system remain poorly understood. Proposed here is a programme to create a much greater understanding in this area through the deployment of a dedicated space asset along with coordinated ground stations, modelling and the creation of a highly accessible database. The space element would comprise 2 co-orbiting spacecraft (TwinSat) involving a microsatellite and a nanosatellite, each including a suite of science instruments appropriate to this study. Over a mission duration of 3 years ∼ 400 earthquakes in the range 6–6.9 on the Richter scale would be ‘observed’. Such a programme is a prerequisite for an effective earthquake early warning system.     

GPS-based sea level measurements to help the characterization of land contamination in coastal areas

The Corsica site has been established in 1996 to perform altimeter calibration on TOPEX/Poseidon and then on its successors Jason-1 and Jason-2. The first chosen location was under the #85 ground track that overflight the Senetosa Cape. In 2005, it was decided to develop another location close to Ajaccio, to be able to perform the calibration of Envisat and in a next future of SARAL/AltiKa that will flight over the same ground tracks. Equipped with various instruments (tide gauges, permanent GPS, GPS buoy, weather station…) the Corsica calibration site is able to quantify the altimeter Sea Surface Height bias but also to give an input on the origin of this bias (range, corrections, orbits, …). Due to the size of Corsica (not a tiny island), the altimeter measurement system (range and corrections) can be contaminated by land. The aim of this paper is to evaluate this land contamination by using GPS measurements from a fixed receiver on land and from another receiver onboard a life buoy. Concerning the altimeter land contamination, we have quantify that this effect can reach 8 mm/km and then affects the Sea Surface Height bias values already published in the framework of the Corsica calibration site by 5–8 mm for TOPEX and Jason missions. On the other hand, the radiometer measurements (wet troposphere correction) are also sensitive to land and we have been able to quantify the level of improvement of a dedicated coastal algorithm that reconciles our results with those coming from other calibration sites. Finally, we have also shown that the standard deviation of the GPS buoy sea level measurements is highly correlated (∼87%) with the Significant Wave Height derived from the altimeters and can be used to validate such parameter.     

A comparative analysis of SLR,MLR, ANN,XGBoost and CNN for crop height estimation of sunflower using Sentinel-1 and Sentinel-2

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (?VH, ?VV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ?VH, ?VV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies.     

Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang,Indonesia by SBAS DInSAR analyses using Envisat-ASAR,ALOS-PALSAR,and Sentinel-1A SAR data

Land subsidence is a critical issue that large cities located in coastal areas, such as Semarang, Indonesia, must address. The monitoring of land subsidence is vital for predicting and mitigating the disasters that such subsidence may cause. Therefore, an economical and effective monitoring method, which can continuously provide accurate measurements over extensive areas, is highly required. Differential Interferometry Synthetic Aperture Radar (DInSAR) has the potential to be a powerful technique that can meet the above demands. Actually, DInSAR has been applied to monitor the subsidence in Semarang, but it was for a limited period before 2012.In order to clarify the transition of the long-term subsidence behavior in Semarang, the Small Baseline Subset (SBAS) method, which is one type of time-series DInSAR, is employed in this research. The sets of data of Envisat-ASAR (2003–2007), ALOS-PALSAR (2007–2011), and Sentinel-1A (2015–2017) are employed for the analyses. Then, the validity of the SBAS results is discussed from the viewpoints of both spatial distribution and temporal transition using GPS displacement measurement results and the geological conditions of the ground.On the other hand, as the lifespan of SAR satellites is commonly designed to be around 5–7?years, an appropriate method, which can connect the subsidence provided independently by the unlinked time-series data sets of the three different SAR satellite data, is required. This study uses the Hyperbolic Method (HM) to connect the above unlinked SBAS results. The HM is often used to fit the monitored subsidence in practice as a geotechnical engineering tool. Using this method, 14?years of the temporal behavior of the subsidence in Semarang is evaluated.It is found that the transition of the subsidence is different depending on the location, and that the subsidence rate is still increasing in the north and northeast parts of the coastal area. This study shows that SBAS DInSAR can be a useful tool for long-term continuous subsidence monitoring.     

Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city,Delhi-India

The present study is an assessment and identification of urban heat island (UHI) in the environment of one of the fastest urbanizing city of India, Delhi Metropolis, employing satellite image of ASTER and Landsat 7 ETM+ in the thermal infrared region 3–14 μm. Temporal (2001 and 2005) ASTER datasets were used to analyze the spatial structure of the thermal urban environment subsequently urban heat island (UHI) in relation to the urban surface characteristics and land use/land cover (LULC). The study involves derivation of parameters governing the surface heat fluxes, constructing statistics of ASTER thermal infrared images along with validation through intensive in situ measurements. The average images reveal spatial and temporal variations of land surface temperature (LST) of night-time and distinct microclimatic patterns. Central Business District (CBD) of Delhi, (Connaught Place, a high density built up area), and commercial/industrial areas display heat islands condition with a temperature greater than 4 °C compared to the suburbs. The small increase in surface temperature at city level is mainly attributed to cumulative impact of human activities, changes in LULC pattern and vegetation density. In this study the methodology takes into account spatially-relative surface temperatures and impervious surface fraction value to measure surface UHI intensity between the urban land cover and rural surroundings. Both the spatial and temporal variation in surface temperature associated with impervious surface area (ISA) has been evaluated to assess the effect of urbanization on the local climate.     

Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Because of global warming, global sea levels have risen, the frequency of drought in Taiwan is much more frequent in winter and spring, and rainfall tends to concentrate in summer. The probability of disaster-type weather has also increased significantly. Estimating precipitable water vapor (PWV) through GPS signals, related studies and analyses of weather conditions, and the effective use of meteorological forecasts have been valued by many meteorological research organizations and officials. In this study, PWV data from 2006 to 2017 and rainfall data were used for long-term harmonic analysis. PWV data calculated by ECMWF (ECMWF-PWV) and PWV data calculated by GPS (GPS-PWV) were subjected to regression analysis to verify the reliability of the GPS-PWV data. The research results show that GPS-PWV and ECMWF-PWV have extremely high correlations; however, the climatic characteristics of some regions and the high spatial resolution of GPS-PWV are able to accurately calculate the high topographic relief of small areas. It is judged that the GPS-PWV is more accurate than the ECMWF-PWV. It is worth noting that the PWV trend of the regions during the 6-year-before period has not changed very much, but the rainfall trend has changed obviously. Except for the eastern region, most of the regions show a decreasing trend year by year. More long-term observations are still needed to prove whether this phenomenon relates to global warming. Long-term rainfall analysis showed that the topography blocked water vapor to the western, southern, and mountainous regions, making them distinctly wet or dry. The harmonic curve showed great consistency with the peaks of PWV and rainfall. However, in the northern and eastern parts of the windward side, the time when maximum rainfall occurred each year may be one month later than the time when the maximum PWV value occurred each year. The reason for this difference is likely to be a decrease in the number of autumn typhoons, resulting in a nearly one-month difference in PWV peaks and rainfall peaks. Finally, we analyzed the linear trend of GPS-PWV and temperature for all regions in Taiwan, and found that annual increasing rate of GPS-PWV and temperature of all regions are within 0.4–0.5 mm/year and 0.04–0.11 C°/year, respectively.     

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang,Indonesia, using multi-sensor satellite data

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68?km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters?=?53%, CVI 7 parameters?=?93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7?km of the total 48.7?km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.     

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.     

Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.     

Displacement monitoring performance of relative positioning and Precise Point Positioning (PPP) methods using simulation apparatus

Besides the classical geodetic methods, GPS (Global Positioning System) based positioning methods are widely used for monitoring crustal, structural, ground etc., deformations in recent years. Currently, two main GPS positioning methods are used: Relative and Precise Point Positioning (PPP) methods. It is crucial to know which amount of displacement can be detected with these two methods in order to inform their usability according to the types of deformation. Therefore, this study conducted to investigate horizontal and vertical displacement monitoring performance and capability of determining the direction of displacements of both methods using a developed displacement simulator apparatus. For this purpose, 20 simulated displacement tests were handled. Besides the 24?h data sets, 12?h, 8?h, 4?h and 2?h subsets were considered to examine the influence of short time spans. Each data sets were processed using GAMIT/GLOBK and GIPSY/OASIS scientific software for relative and PPP applications respectively and derived displacements were compared to the simulated (true) displacements. Then statistical significance test was applied. Results of the experiment show that using 24?h data sets, relative method can determine up to 6.0?mm horizontal displacement and 12.3?mm vertical displacement, while PPP method can detect 8.1?mm and 19.2?mm displacements in horizontal and vertical directions respectively. Minimum detected displacements are found to grow larger as time spans are shortened.     

High resolution science with high redshift galaxies

We summarize the high-resolution science that has been done on high redshift galaxies with Adaptive Optics (AO) on the world’s largest ground-based facilities and with the Hubble Space Telescope (HST). These facilities complement each other. Ground-based AO provides better light gathering power and in principle better resolution than HST, giving it the edge in high spatial resolution imaging and high resolution spectroscopy. HST produces higher quality, more stable PSF’s over larger field-of-views in a much darker sky-background than ground-based AO, and yields deeper wide-field images and low-resolution spectra than the ground. Faint galaxies have steadily decreasing sizes at fainter fluxes and higher redshifts, reflecting the hierarchical formation of galaxies over cosmic time. HST has imaged this process in great structural detail to z  6, and ground-based AO and spectroscopy has provided measurements of their masses and other physical properties with cosmic time. Last, we review how the 6.5 m James Webb Space Telescope (JWST) will measure First Light, reionization, and galaxy assembly in the near–mid-IR after 2013.     

PS-InSAR技术在伯克利山滑坡监测中的应用

针对伯克利山的滑坡问题,利用从1992年到2010年期间获得的大量ERS1/2,Radarsat-1,TerraSAR-X星载雷达数据,提出运用永久散射体(PS-InSAR,Persistent Scatterer Interferometry Synthetic Aperture Radar)技术,以一幅主图像为基准,与其他SAR图像分别进行差分干涉处理,并限定幅度和相位双重阈值选择PS点,分析其相位在时域空域的频谱特性,最后通过滤波处理提取形变部分,结果成功地定位了伯克利山的滑坡区域,得到了地形的形变速率,为今后的灾害预警打下了基础.     

Quantitative contribution of climate change and anthropological activities to vegetation carbon storage in the Dongting Lake basin in the last two decades

The Dongting Lake Basin is an important hydrological regulation and flood storage area in the Yangtze River Basin, which plays an important role in maintaining regional ecological security. The watershed vegetation and its carbon sequestration capacity have changed dramatically due to climate change and human activities in the last two decades. In this paper, the monthly and annual vegetation net primary productivity (NPP) of the Dongting Lake basin during 2000 to 2020 was firstly estimated using the improved Carnegie-Ames-Stanford Approach (CASA) model. Then the multi-year NPP change trend and its significance were analyzed based on Theil-Sen median and Mann-Kendall method. Subsequently, the Hurst index was used to simulate the vegetation NPP persistence in the study area. Finally, the driving mechanisms of vegetation NPP changes in the study area were explored using partial correlation coefficients and residual analysis. The results demonstrated that: 1) The annual average NPP in the basin showed a fluctuating and increasing trend from 273.54 to 718.30 g C/m²·yr¹ during 2000 to 2020, and except for autumn, all seasons (spring, summer, winter) and annual NPP series showed an upward trend. The spatial distribution of vegetation NPP is characterized by an asymmetrical horseshoe shape in general, with high values in the west, south and east parts, and low values in the Lake area; 2) During the study years, about 84.38 % of the basin area showed a significant and extremely significant increase of NPP; 3) The future trend of vegetation NPP in the basin shows that the area of decrease (22.79 %) is more than the area of increase (11.35 %). The Chang-Zhu-Tan urban agglomeration will generally show a continuous and extremely significant reduction trend, while parts of Dongting Lake will show a continuous and extremely significant increase trend; 4) The correlation between solar radiation and NPP is stronger than the other two meteorological factors (precipitation and temperature). Temperature has a significant inhibitory effect on NPP, solar radiation has a significant promoting effect on NPP, and the effect of precipitation on NPP was relatively complicated. The relative importance of meteorological factors on vegetation NPP was ranked as follows: solar radiation > precipitation > temperature. The impacts of both climate change and human activities on NPP changes showed great spatial variability, and the positive contributions (89.81 % for climate change and 82.87 % for human activities) were both greater than the negative contributions.