Conference Agenda

One of the objectives of Dragon 4 Project 31470_2 is the investigation of upscaling and adaptation models and algorithms for 3D multi-functional and scales forestry inventory by using airborne data and products as basis.

The main achievements acquired during the last years could be summarized as follows:

(1) Improvement of forest carbon flux simulation by incorporating remotely sensed model with process-based model.

The improved simulation of forest carbon fluxes was conducted by incorporating a remote-sensing-based MODIS MOD_17 GPP (MOD_17) model with a process-based model (Biome-BGC) using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17) model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis.

Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the pre-selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC) and tree ring measurements than the original model did. To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF) was used to assimilate Global LAnd Surface Satellite (GLASS) LAI products into the calibrated Biome-BGC model. Finally, the calibrated and data-assimilated model was applied to simulate the large scale and long-term forest carbon fluxes.

(2) Estimation of forest structure parameters by using multi-source remotely sensed data

As important forest parameters, the leaf area index (LAI), canopy closure (CC), forest height (h) and forest above-ground biomass (AGB) are indispensable for ecological process models and carbon cycle models. Therefore, the accurate estimations of regional or global scale forest parameters are of great significance for a deep understanding of inherent laws of environmental change. With the diversification of remote sensing technology, the single-source remote sensing data has been unable to meet the application demand of the region and high precision. Recently, a large amount of effort has been devoted to the joint utilization of multi-source remote sensing data for the estimation of regional forest parameters. However, the regional application, topographic influence, and mixed pixel decomposition have become the three major scientific problems in the joint retrieval of the multi-source remote sensing data. In response to these three problems, this study has proposed methods for the prediction of the mountain forest height, the canopy closure, and the effective leaf area index (LAIe). Furthermore, the forest AGB model was constructed based on vegetation indices, topographic indices and these structure parameters with physical significance.

The research includes the following three main aspects:

1) Predicting forest height using the GOST model and multisource remote sensing data for sloping terrains.

2) Predicting canopy closure and effective leaf area index using the Li-Strahler geometric-optical model and multisource remote sensing data.

3) Multi-parameter synergic retrieval of forest AGB.

(3) Monitoring forest change by integrating active and passive remote sensing data

Accurate and rapid acquisition of forest land change information is critical for the study of ecological environment changes and forest management planning. At this aspect, multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and Chinese Gaofen-1/2 (GF-1/2) optical images were applied to detect the forest changes. For SAR images, the difference image was extracted by using the improved log-ratio method. The Bayesian theory based minimum threshold error adaptive threshold selection method (Kilter and Illingworth, K&I) was used to segment the threshold and extract the change areas. For GF images, the difference image was extracted by using the multivariate change detection (MAD) algorithm, and the maximum inter-class variance method (OTSU) was used to segment the threshold and extract the change areas. Finally, incorporating the change detection results of above two tests was conducted to determine the local forest land changes. The validation based the field survey showed that the incorporation of active and passive remote sensing techniques can efficiently and timely detect the forest land changes with high spatio-temporal resolution, due to high temporal resolution (12 days) of Sentinel-1 and high spatial resolutions (GF-1:2m, GF-2:1m) of GF-1/2 data.

(4) Modeling forest above-ground biomass dynamics using multi-source data and incorporated models

Forest dominates the terrestrial carbon cycle and forest above-ground biomass (AGB) has been the critical index for carbon sequestration capacity. However, any individual method, such as ground-measurement-based method, remote-sensing-based method, and ecological model-based model, cannot efficiently describe the changing processes and driven mechanisms of forest AGB dynamics. Based on multi-mode remote sensing, time-space dynamic knowledge of forest ecological process, and continuous multi-disciplinary ground observation data, this project is planning to model spatial-temporal continuous, physical quantity-synergy forest AGB dynamics. Firstly, a highly accurate regional forest AGB product obtained by applying multi-mode remote sensing and scaling connection is used as the AGB basis. Then, the uncertainties of simulation of forest growth processes are alleviated by use of model-model and model-data fusion strategies. Finally, modeling of forest AGB dynamics is accomplished by combining forest AGB basis with succeeding dynamic forest growth processes, which taking the effects of tree mortality, forest disturbance into account. The methodology of spatio-temporal synergetic modeling of Forest AGB dynamic information proposed by this project, can explore the eco-physiological mechanisms of spatio-temporal pattern of forest AGB dynamics and the driven forces of natural and anthropogenic disturbances. Moreover, this methodology can extend the spatial and temporal dimensions of forest AGB dynamics and in order to precisely improve forest quality and promote the national ecological civilization.

Keywords: Forest above-ground biomass, carbon cycle, model coupling, data assimilation, spatio-temporal synergy

Synthetic Aperture Radar Tomography (TomoSAR) is a microwave imaging technology to focus the illuminated scatterers in the 3D space, by jointly processing multiple acquisitions from parallel trajectories. TomoSAR has been applied with success to the 3D analysis of forested environments.

In principle, TomSAR can be easily understood by considering that the availability of multiple flight lines allows the formation of a 2D synthetic aperture, which permits to focus the signal not only in the range-azimuth plane, as in conventional 2D SAR imaging, but also in elevation.

Although the concept is straightforward, the application of TomSAR using spaceborne sensors is hindered by the fact that different baselines are usually acquired at time lags on the order of days, limiting the analysis to temporally stable targets (like urban scenarios). A possible way out of this blocking circumstance is the employment of single pass interferometers, as in the case of Tandem-X (currently operating) and possible future systems.

Such systems achieve the 3D imaging capabilities by collecting a number of simultaneous interferometric pairs acquired by two satellites. The observed complex coherence corresponds to a particular vertical wavenumber of the imaged scene, depending on the interferometric baseline, i.e., the across-track distance between the two satellites. By collecting multiple pairs with varying interferometric baseline it is then possible to get multiple vertical wavenumbers, which allows the reconstruction of the vertical distribution of the backscattered power of the imaged scene through spectral estimation techniques.

Such tomographic data acquired using spaceborne sensors are characterized by some specific features that may limit the performance of classical 3D focusing techniques. Such data are generally gathered into stacks of a limited number of images, having a coarse spatial resolution, and specific correlation properties. As a result, the available 2^nd order statistical information is largely incomplete and lacks of the redundancy used by classical spectral analysis techniques to enforce a sufficient output signal quality. The achievable vertical resolution is, in general, extremely coarse, due to a limited spatial resolution of the individual SAR images and to a low-pass effect of the spectral interpolation techniques used to reconstruct the missing information.

This contribution summarizes some solutions to these intrinsic limitations. The coarseness of the naturally available vertical resolution, obtained using classical Fourier focusing, is partially compensated with super-resolution techniques based on the processing of a reconstructed covariance matrix. An improved reconstruction of a positive semi-definite covariance matrix is achieved using an original multi-resolution technique which ensures a good conditioning of the estimated information all-along the evaluation process. The validity and usefulness of this approach in the polarimetric mode is assessed using simulated spaceborne data sets obtained from airborne ESA campaigns

Accurate and large-scale access to forest height information is of great significance for the fine management of forests, carbon cycle modeling and scientific research on climate change. Interferometric synthetic aperture radar (InSAR) data without or with very low temporal de-correlation is sensitive to the vertical structure of vegetation and is one of the most potential remote sensing technologies to map forest height in large area. It has been demonstrated by few studies that TanDEM-X InSAR data can be used to map forest height by applying RVoG model or InSAR water-cloud model if the terrain was not so steep, otherwise, descending and ascending InSAR data should be used together for generating one wall-to wall map of an interested region. However, we still need much more detailed investigations on this topic for area of steep terrain in order to apply them to practical mapping activities. So we established two test sites in the Northeast forest region of China: Chaozha forest farm in Genhe district and Wangyedian forest farm in Chifeng district. Chaozha test site is relatively flat, while Wangyedian test site is of steep terrain. Firstly, the performance of forest height inversion using the difference method (DIFF method, in short, taking the forest height as the difference between the DSM from Tandem-X and the DEM from LiDAR) and the SINC model based method (SINC method, in short, where SINC model is one simplified Random Volume over Ground model) was analyzed. Secondly, the effects of signal-to-noise ratio (SNR) de-correlation, spatial baseline, ground-to-volume scattering ratio and extinction coefficient on the estimation of SINC model were studied; Finally, the influence of terrain on the SINC model was investigated, and a threshold determination method was proposed based on Monte Carlo simulation and RVoG model, so as to provide a basis for masking the region severely affected by terrain and for doing multi-track data fusion further. We established a technical process for estimating forest height from space-borne InSAR data without temporal de-correlation under steep terrain conditions, which will provide very useful technique supporting for forest resources monitoring and forestry management activities.

Forest stand height is one of the most important parameters in forest inventory and has a close relationship with other parameters (such as DBH, biomass). Nowadays airborne laser scanning is considered the most accurate remote sensing method for forest height extraction. However, these airborne surveys are relatively expensive and there is a desire to identify more affordable options for collecting or updating this information. In this paper, ZY-3 satellite stereo images are used to derive a digital surface model, which together with a high-resolution digital terrain model (DEM) from airborne laser scanning (ALS) to estimate forest stand height. Forest stand height derived from LIDAR is used as the reference for validation. The results show that ZY-3 stereo satellite images are suitable to extract forest stand height with reliable accuracy when a high-resolution DEM is available.

I. INTRODUCTION

This paper proposes a decomposition technique that accounts the influence of coherent and incoherent double-bounce scattering mechanisms. In order to assess our physical understanding of the interaction between an emitted radar wave and a forested area, a man-made miniaturized RVoG-like scene is imaged. Consisting of a volume lying above a ground, this scene highlights the presence of ground/volume double-bounce and ground/trunk double-bounce.

II. Validation on in-situ data

An electromagnetic wave encounters four potential scattering mechanisms in a forest. The single-bounce on ground, double-bounce ground/trunk, double-bounce ground/volume and volume scattering.

The equivalent distance of a wave encountering a double-bounce is the distance of a single-bounce originating from the ground. Meaning that the double-bounce mechanism is considered as a ground response due to the fact that classical imagery algorithms will represent it on the ground. Taking all potential double-bounces along the volume, it follows that a projection of volume contributions will be located on the ground beneath the volume.

Bare soil contributions k_s are therefore estimated by subtracting double-bounce contributions k_st+k_sv from total ground response, i.e. k_s = k_g − (k_st+k_sv).

PolTomSAR (Polarimetric SAR Tomography) acquisitions over a man-made miniaturized RVoG-like scene show that ground/trunk double bounce is coherent and that ground/volume double-bounce is incoherent. Existing decomposition techniques such as SKP (Sum of Kronecker Products) or HySKP (Hybrid SKP) introduced respectively in [1] and [2] are therefore able to separate the coherent double-bounce from the ground but the incoherent double-bounce remains.

The proposed approach to separate this incoherent double-bounce is to estimate volume contributions and subtract a portion of these contributions from the ground by using the Freeman decomposition for volume estimation [3].

REFERENCES

[1] S. Tebaldini, "Algebraic Synthesis of Forest Scenarios From Multibaseline PolInSAR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 12, pp. 4132-4142, Dec. 2009. doi: 10.1109/TGRS.2009.2023785

[2] M. Pardini and K. Papathanassiou, "On the Estimation of Ground and Volume Polarimetric Covariances in Forest Scenarios With SAR Tomography," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1860-1864, Oct. 2017. doi: 10.1109/LGRS.2017.2738672

[3] A. Freeman and S. L. Durden, "A three-component scattering model for polarimetric SAR data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 3, pp. 963-973, May 1998.

doi: 10.1109/36.673687

Desertification is one of the most important environmental problems in
China's arid regions, and the damage is very serious. Soil organic matter (SOM)
content is an effective indicator for effectively reflecting desertification status. Large
area monitoring of organic matter content is of great significance for mastering the
status and dynamics of desertification and formulating scientific and effective
prevention strategies. However, due to data limitations, vegetation signal interference,
etc., large-area acquisition of SOM content has always faced great difficulties. The
purpose of this paper is to estimate the soil surface SOM content in desertified land in
northern China by using Sentitle-2 data mainly based on the Google Earth Engine
(GEE ) platform. Two machine learning methods, classification and regression tree
(CART) and support vector machine (SVM) respectively, were employed with
different input variable combinations. The results show that the proposed approach
could estimate SOM in desertified land effectively. CART shows higher accuracy than
SVM, especially when Sentinel-2 band reflectance and ancillary factors (vegetation
index, elevation, annual average temperature, etc.) were totally selected as input
variables, the model’s R²could reach up to 0.73, RMSE can reach 0.31. Also, the
approach based on GEE has the advantage of time efficiency, less than1 hour is
needed to finish the estimation of the SOM at the 10 m spatial resolution for the entire
desertified land in northern China, which has great potential for high spatial resolution
SOM mapping at the large scale.

Observations show that in recent decades, a large area of China has been affected by drought and that frequent droughts have caused damage to the ecological environment and the economy. Because of the differences in data and methods, assessing regional droughts often leads to contradictory conclusions. The self-calibrated Palmer drought severity index (scPDSI) is based on multiple parameters, such as precipitation, temperature and soil properties, and it is considered regionally applicable and is widely used. However, some divergence has been observed in the results of drought in China using different scPDSI_PM (scPDSI based on the Penman-Monteith model) datasets. We establish an integrated scPSDI dataset (scPDSI_PM _INT) by averaging three scPDSI products through the equal-weighted method and analyze the temporal change in and spatial characteristics of drought in China from 1950 to 2009. The annual and seasonal drought intensities have increased in the past 60 years. The disturbed area has broadened significantly, especially in eastern China, which has become much drier. The intensity of most drought-prone regions is abnormally dry and moderate, while severe and extreme droughts occur mostly in the agro-pastoral zone and the Beijing-Tianjin-Hebei region. The vegetation activity of response to regional drought was analyzed in this research.

The objective of Dragon 4 Project 32396_2 aims at detecting land degradation in dry lands at a regional scale. The main achievements acquired during the last years could be summarized as follows:

(1) Assessment and monitoring of land degradation in dry lands of China: T With the development of remote sensing technology, long time series remote sensing data have been available for land degradation assessment and monitoring, and the vegetation indicators, such as the NDVI, NPP, Vegetation coverage and biomass were commonly used. However, time series vegetation index will fluctuate severely due to the impact of climate change, especially the fluctuation of annual precipitation, thereby the land production capacity could not be determined accurately. Therefore, to solve the problem, Xilin Gol League, Inner Mongolia Autonomous Region, China, where the land degradation is prevailing in the first decade of the 21st century was selected as the study area. Based on the annual NPP dataset estimated by 10-Day composite NDVI from Envisat-Meris data at 1.2km resolution during 2003 to 2013 and the same period meteorological raster dataset, a new Moisture-responded Net Primary Productivity (MNPP) method, for identifying areas of land degradation based on the change of annual NPP and MNPP over time and Moisture Index (MI) was developed. It was expected that provide technical support and scientific reference data for land degradation assessment and monitoring in study area, even in the whole drylands in China.

(2) Estimating Soil Organic Carbon Density in the Otindag Sandy Land, Inner Mongolia, China. Accurate quantitative estimates of soil organic carbon density (SOCD) can effectively represent regional carbon cycle processes and regulation mechanisms, and can serve as reference data when making land management decisions. Limited research, however, has been carried out in arid or desert zones covered with sparse vegetation, despite the fact that these cover wide areas of the earth and play a significant role in global carbon cycles. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) in the Inner Mongolia Autonomous Region of China was selected as the study area. The study introduces a useful technique for making high spatial coverage SOCD estimates for drylands by utilizing GF-1 WFV optical satellite images and a time series of MODIS satellite remote sensing datasets, and using these to optimize parameters for simulation models in conjunction with other technical procedures that are described. We expect this research to provide useful technical support and scientific reference data for land management and for land degradation/desertification assessments, for the study area monitored, as well as across the whole dryland area of China.

(3) Estimating Above Ground Biomass in the Otindag Sandy, Inner Mongolia, China by Using Sentinel-2 data. Above ground biomass (AGB) is an important measure of terrestrial ecosystem productivity, and it is used in quantifying the role of vegetation in the carbon cycle, the potential for energy production, and the carbon stock estimation for climate change modelling. Dryland AGB, also recommended as the indicator of land productivity by UNCCD in desertification assessment and monitoring, need to be quantitative assessed and evaluated. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) was set as the study area and a useful method for sparse vegetation aboveground biomass inversion in dryland was promoted. Firstly, the Sentinel-2 remote sensing data coved the whole area in growing season (May to September) during 2015 to 2018 and synchronous field survey data was collected and processed. Then, the estimation model was constructed by linear regression model, power function model, exponential model and machine learning model by taking band information, texture information and different vegetation index into consideration. In addition, total 2/3 field survey sampled AGB data were used for modelling, and the remaining 1/3 measured AGB data was set as the testing to evaluate the AGB estimation models. Finally, the AGB distribution of the OSLAIS was mapped and analysed based on the optimal model. This research is expected to provide technical support and scientific reference data for vegetation assessment and monitoring in the study area, and even across the entire dryland of northern China.

Land condition results from land use in a managed territory. However, the reverse holds too: only a subset of land uses can be applied given a state of land condition. Such reciprocal feedbacks can be of utmost importance for assessing the land management options of a territory.

This was the main hypothesis of this study. To test it, we explored associations and dependencies between land condition states and land cover classes in the China drylands. More precisely, the study area was the Potential Extent of Desertification in China (PEDC), determined after applying the FAO-UNEP aridity index to an archive of climatic surfaces. The study period was 2002-2012. Land condition states resulted from the application of the 2dRUE method to an archived time-series of Net Primary Productivity (NPP), derived from MERIS satellite data by the CASA algorithm. Such states describe ecological maturity in terms of aboveground vegetation biomass and turnover, and lend well to an ordinal scaling. Land cover classes resulted from the aggregation of thirty-eight classes of level II built for China for the year 2010 following the Land Cover Classification System of the FAO. Land uses were excluded from this preliminary run. The spatial resolution for all the analyses was of 4 km.

We performed two statistical tests on the described data set, stratified by aridity zones. First, associations between land condition states and land cover classes were determined by chi-square tests, using the Monte Carlo method. Wherever significant associations were found between these variables, we interpreted the standardized residuals to determine the significance and sign of individual combinations of the corresponding contingency table. The second test was a non-parametric ANOVA with unequal samples, using the Kruskal-Wallis and Median test. We also determined homogeneous groups of land cover classes (in terms of land condition) through non-comprehensive search of land condition differences in pairwise combinations of them.

The associations between land condition and land cover resulted significant for all the dryland aridity zones. In general, areas of low vegetation cover such as desert or bare soil were positively associated with more degraded states, whilst higher vegetation cover was positively associated with states of higher maturity and complexity. As for the second test, it was significant too and two homogeneous groups of land cover could be formed for all the aridity levels.

The results, as reported in this abstract, are somewhat limited because of the exclusion of proper land uses. The land cover classes used here were only five (Deserts and bare soils, Grasslands, Shrubs, Open forests and Forests) and these are likely to be controlled in balanced terms by physical gradients and human intervention. Still, 2dRUE detects land condition states after a climate correction, and both mature and reference states have been found in real deserts of North Africa, for example. This suggests that significant associations in this study between class Deserts and land degradation involves human management to some extent. In other words, this class might be a mixture of proper zonal deserts and desertified areas that were possibly with a denser vegetation cover in earlier times.

The approach will be repeated using a full classification of land uses. Meanwhile, we can preliminarily conclude that it produces interpretable results that will help determining interconversion pathways between land cover classes, which in turn supports the paradigm that land degradation can be defined as loss of management options.

Approximately half of the world’s population is at the risk of at least one vector-borne parasitic disease. The survival of intermediate hosts of vector-borne parasitic diseases is governed by various environmental factors, and remote sensing can be used to characterize and monitor environmental factors related to intermediate host breeding and reproduction, and become a powerful means to monitor the vector-borne parasitic diseases. Schistosomiasis is a parasitic disease that menaces human health. Oncomelania hupensis (snail) is the unique intermediate host of schistosoma, so monitoring and controlling of the number of snail is key to reduce the risk of schistosomiasis transmission. Landsat 8 and Sentinel 2 had been used to obtain the environmental factors (vegetation, soil, temperature, terrain et al.), which are related to the living, multiplying and transmission of intermediate host. Then this study used T-S Fuzzy RS model to establish a new suitable index membership function due to the different RS data, and a long time series dynamic monitoring of snail distribution in Dongting Lake were achieved. A comparative analysis had been performed to validate the predicted results against the field survey data. The results demonstrate the accuracy of the developed model in predicting distribution of snails.

Malaria has induced enormous public health problems worldwide, especially in the tropical and subtropical areas. The transmission of malaria parasites depend on mosquitoes’ biting on human beings. The ability of the mosquitoes to transmit malaria parasites is dependent upon a series of biological features generally referred to as vectorial capacity. The development of mosquitoes’ population as well as their biting behaviors are determined by a serial of environmental factors, especially the rainfall and temperature. In this study, remote sensing products from the high-resolution GF-1 images were utilized to develop the vectorial capacity model (VCAP), which was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. The developed model was implemented in Tengchong City in Yunnan Province, which is located at the China-Myanmar border area. The data of historical malaria infections, as well as the meteorological and hydrological records were collected to establish geographic information system database in terms of spatial distribution of malaria and mosquitoes．Then spatial pattern of mosquitoes’ vectorial capacity were mapped and the risky area for malaria transmission were identified, which will help to develop more sustainable strategies for malaria control and prevention．

Schistosomiasis is one of the most serious parasitic diseases due to the infection of schistosoma japonieum via the intermediate host snails, which have endangered to the safety of public health worldwide. In China, it remains endemic in lake and marshland regions including Anhui, Hubei, Hunan, Jiangxi, Jiangsu Provinces as well as mountains areas in Sichuan and Yunan Provinces. The transmission of schistosomiasis is closely related to environmental factors, such as vegetation, temperature, hydrology and soil. In order to enhance the capacity of schistosomiasis contro1 and prevention, TM images and high-resolution GF-1 images were utilized to identified snail habitats of based on geographic information system (GIS) and remote sensing(RS) technology. As a typical lake and marshland endemic regions, Yueyang City in Hunan Provice which is located near the Donting Lake, is selected as the study region. The data of historical infection records, as well as the meteorological, hydrological and land surface types were collected to establish geographic information system database of spatial distribution of schistosomiasis and snails．Then spatial pattern of schistosomiasis risks were mapped and factors associated with geographical variation in infection patterns were identified, which will help to develop more sustainable strategies for schistosomiasis surveillance．

The AMEOS (Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting) project aims at providing a tool for pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture.

The previous two years of the project were devoted to delineate the procedure enabling the satellite imagery based monitoring of crop pests and diseases. In particular, with reference to the Xylella fastidiosa threats of olive groves the approach consists in:

• Improving the classification of the agricultural areas devoted to olive trees, starting from what has been made available from the Corine Land Cover initiative;

• Developing an approach suitable to be automated for estimating trees by using Sentinel 2 images. This tool will be used to monitor the spread of pest.

• Developing an approach suitable to be automated for counting trees by using very high spatial resolution images in areas at high risk of infection.

In the previous year, working on Sentinel-2 images and exploiting the characteristics olive tree phenology and carotenoid indices allowed to improve, respect to the Corine Land Cover, the classification of the olive groves in the area of interest. Further, the use of a morphological approach on NDVI computed by using Sentinel-2 images of 2017 allowed to assess the olive groves density and trees number for each crop field. The quality of the results were validated by using a VHR image.

The present paper concerns the analysis of the possibility to monitor the decrease in the number of olive trees as a consequence of the spread of the xylella fastidiosa disease. The analysis has been carried out on Sentinel-2 images acquired in the second half of 2018.

The final objective of the project is the development of a satellite based system capable to assess the evolution of diseases on both permanent crops (olive groves, vineyards) and row crops (wheat, maize), in Italy and China, aiming at developing an early working tool.

Pathologically, the foliar biophysical variations are critical indicators for tracking the progressive host-pathogen interactions at different development stage. The interaction of electromagnetic radiation with plant leaves is governed by their biophysical constituents, and response to infestations. Hyperspectral- and multispectral- continuum observations could permit the acquiring of the host-pathogen processes within entire epidemic stages.

With this in mind, the launch of the hyper-spectral satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa, HyperSpectral Precursor of the Application Mission, to be launched on the 15^th of March 2019), could give a significant boost to the achievement of the project objectives.

The leaf area index (LAI) is a key parameter for describing the crop canopy structure and is of great importance for crop disease monitoring and yield estimation. Light detection and ranging (LiDAR) is an active remote sensing technology that can detect the vertical distribution of a crop canopy. To quantitatively analyze the influence of the occlusion effect, three flights of multi-route high-density LiDAR dataset were acquired using a UAV-mounted RIEGL VUX-1 laser scanner at the altitude of 15m, to evaluate the validity of LAI estimation in different layers under different planting densities. The result revealed that normalize root-mean-square error (NRMSE) for the upper, middle, and lower layers were 4.0%, 9.3%, 15.0% of 88050 plants/ha, respectively. In order to investigate the effect of different incidence angle, the accuracy of the point cloud data inversion for different air routes (different angles of incidence) was compared and found that the optimal incidence angle was −12° to −5°, and the NRMSE for the upper, middle, and lower layers were 9.3%, 8.0%, 11.5%. The voxel-based method was used to invert the LAI, and we concluded that the optimal voxel size was 0.05 m, which is approximately 2.09 times of the average point distance. The detection of different layers of different planting densities, incidence angle, and optimal voxel size can provide a guideline for UAV-LiDAR application in the crop canopy structure analysis

Yield estimation and forecasting, at the field scale, would allow farm managers to plan their agronomic operations, e.g., sowing, tillage or fertilization, on the basis of expected yield potential. At the district scale, it would be useful for public and private organization, for commercial or planning purposes. In order obtain in-season crop yield predictions, biophysical variables retrieved from remotely sensed data can be assimilated into crop growth models. Data assimilation is a group of methods allowing to combine in an optimal way different information types, dynamically integrating mathematical descriptions of processes embedded into deterministic models and physical information obtained from observations. It allows to update model simulations with observed data, e.g. from remote sensing, within a systematic estimation structure. The most advanced assimilation methods include a framework for the incorporation of model and observation errors and the quantification of prediction uncertainty.
The Ensemble Kalman Filter (EnKF) method has been shown to be particularly suitable for the assimilation of remotely sensed data into crop models and has been extensively assessed for this purpose. The efficiency of EnKF has proved to be affected by a range of factors, such as the number of observations ingested into the process. Many studies have shown that using a higher number of observations brings about an improved accuracy of estimation. Other factors such as errors and uncertainties in the remote sensing observations, the variables that are retrieved as well as crop models formulation errors and parameters uncertainties play also an important role.
The project presented in this study, carried out in the framework of the Dragon 4 Programme supported by the European Space Agency (ESA) and the Ministry of Science and Technology (MOST) of China, aims at targeting these methodological issues, affecting the operational application of data assimilation schemes to the precision farming context.
A study was carried out in Maccarese (Central Italy) for the purpose of assessing the effect of temporal and spatial resolution of multispectral satellite imagery on the accuracy of durum wheat yield data estimation at the field scale from EnKF data assimilation. Field campaigns to measure biophysical variables were carried out from January 2018 to April 2018, for different growing conditions of the crop, at dates close to Sentinel-2 and Venus satellite acquisitions. The latter is a French-Israeli mission combining a high spatial (5 m) and temporal (2 days) resolution, with spectral bands quite similar to Sentinel-2. Initially, an assessment of different hybrid methods, i.e. based on the training of non-parametric regression algorithms with PROSAIL model simulations, was made, in order to identify the most accurate technique for the retrieval of biophysical variables from satellite imagery. Subsequently to a validation study with ground data, the retrieval technique was applied to all the cloud-free images available for the wheat season, notably 25 for Sentinel-2 and 26 for Venus. Among all the biophysical variables, fractional vegetation cover (FCOVER) was estimated with a smaller error (RRMSE of 9.5%) as compared to LAI (RRMSE of 19%). The former variable was thus employed for the data assimilation using the Acquacrop crop model developed by the Food and Agriculture Organization of the United Nations (FAO), since this model uses FCOVER as the main dynamic crop state variable. Recently an open source code version, Aquacrop-OS, has been released, thus allowing the application of sequential data assimilations methods, such as the Ensemble Kalman Filter, which was previously impossible.
EnKF was thus applied to Aquacrop-OS using a) only observations from Sentinel-2, b) only observations from Venus, or c) observations from both satellites, for durum wheat fields of the Maccarese farm. The results, in terms of grain yield prediction, were compared to fields where yield map data had been collected from the combine harvester at the end of the season. In general, benefits from an increased spatial and temporal resolution for field scale yield prediction mapping could be observed, but these came at the cost of an increased computational demand, therefore suggesting that a balance would be required for an operational application in the context of precision agriculture.

The development of high-quality specialty grain and protein-based classification of different types of grain is an important measure, which accelerates the shift from agricultural production to quality improvement. Remote sensing technology had achieved the prediction of grain protein content (GPC), but there were still large deviations in the interannual expansion and regional transfer. The study was conducted in wheat growing areas of Beijing, Renqiu and Jinzhou in Hebei Province. Firstly, Spectral consistency of Landsat-8 and RapidEye respectively compared with Sentinel-2 satellites at the same ground point in the same period. Then, based on the hierarchical linear model (HLM) method, the GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Mediumrange Weather Forecasts (ECMWF). The prediction of regional GPC and its spatial expansion were validated. Results were as follows: (1) spectral information calculated from Sentinel-2 imagery were highly consistent with that from Landsat-8 (R² = 1.00) and RapidEye (R² = 0.99) imagery could be jointly used for GPC modeling. (2) Predicted GPC by using HLM method (R² = 0.55) demonstrated higher accuracy than empirical linear model (R² = 0.23) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in Xiaotangshan and Renqiu City were ideal with RMSEv of 0.91% and 0.49%, respectively. This study had great application potential for regional quality prediction and inter-annual quality prediction.

Wheat powdery mildew is caused by the fungus Blumeria graminis and is one of the most common diseases that result in significant loss of crop yield and quality in China. Accurate monitoring of wheat powdery mildew at the regional level is important for food security and environmental protection. Traditionally, wheat powdery mildew is monitored by visual inspection of individual plants, which is time-consuming and inefficient. In recent years, a new satellite-based remote sensing technology has become a more viable option for managing and controlling agricultural practices.

Wheat powdery mildew is monitored by remote sensing technologies based on changes in transpiration rate, chlorosis, leaf color, and morphology in infected plants, which in turn affects the spectral reflectance properties of wheat. The existing methods includes statistical analysis and machine learning methods. In general, wheat powdery mildew can be detected visually for only a short period of time and the field sampling is often expensive. Thus, these methods do not always meet the needs of agricultural management due to the difficulty of collecting field samples.

Limited training data is a common problem in remote sensing applications and many studies have applied transfer learning in remote sensing to increase the quality and quantity of samples. However, thus far, the use of transfer learning techniques for crop disease monitoring has received limited attention. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region.

This study was carried out in Gaocheng city, Hebei province, and the auxiliary field survey samples were acquired from western Guanzhong Plain, Shaanxi province. The samples were categorized into three groups, i.e., normal, slightly diseased and seriously diseased, according to the disease index (DI). The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wetness and greenness were extracted using Landsat-8 OLI images to indicate the growth status of wheat and the field habitat characteristics. With these VIs, TrAdaBoost, which using the support vector machine with radial basis function kernel (RBFSVM) as weak learner, was used to develop the wheat powdery mildew monitoring model. At the initialization of the algorithm, an initial weight vector was given, and the maximum number of iterations was also determined. Per iteration, some samples were taken out from the auxiliary samples and study area samples, respectively. The removed samples were used to train a RBFSVM, and the weight of a sample indicated the probability of this sample to be selected to train the RBFSVM. At the end of each iteration, the error of RBFSVM on each training sample was calculated. If an auxiliary training sample was wrongly predicted, the sample likely conflicted with the study area sample. Then, we decreased its weight to reduce its effect, where in the next round, the misclassified auxiliary training sample would affect the learning process less than the previous round. In contrast, if a study area training sample is wrongly predicted, we increased its weight to improve its effect in the next round. Through this mechanism, the auxiliary samples that were useful for improving the monitoring accuracy of wheat powdery mildew in the study area were selected.

The model was tested using a dataset with 53 study area samples and 39 auxiliary samples. The overall monitoring accuracy was 83%, and the kappa coefficient was 0.69. Moreover, TrAdaBoost was also compared with four algorithms that are commonly used to monitor wheat powdery mildew at the regional level, and TrAdaBoost performed better than other algorithms. Experimental results demonstrated that TrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

Since March the 22th 2019 the PRecursore IperSpettrale della Missione Applicativa – PRISMA is in orbit on a sun-synchronous orbit at 615km. The PRISMA mission, fully developed by the Italian Space Agency (ASI), combines two payloads: a hyperspectral and a panchromatic camera. PRISMA, together with the Chinese Gaofen-5 (GF-5), are the only hyperspectral sensor in orbit with similar characteristics in term od GSD and spectral resolution.

The PRISMA hyperspectral payload is a pushbroom scanner covering the full range (VNIR-SWIR) from 400 to 2500nm with 239 spectral bands at a spatial resolution of 30 m with a swath of 30 km, while the panchromatic camera provides 5m pixel images co-registered with hyperspectral imagery. PRISMA is a prism spectrometer and it is characterized by a variable bandwidth across the nominal spectral range, nevertheless the band width is less than 12nm (i.e. between 7.3 and 11.04nm). The PRISMA coverage is global with a 29 days (orbit repeat period), while the revisit time on a specific area of interest is of 7 days thanks to the off-nadir angle of up to ± 20.7°.

After the end of the commission phase (scheduled in the 2^nd semester 2019), it is foreseen a structured three years CAL/VAL activity, which will be performed on scattered instrumented sites in Italy. The test sites have been selected according to the peculiar thematic areas of interest for the mission (e.g., topsoil characteristics, vegetation biophysical parameter retrieval, snow and coastal waters), moreover international test site are still under definition. The Cal/Val activities includes: airborne surveys with VNIR-SWIR scanner eventually coupled with thermal LWIR multispectral data, field activities contemporary to the PRISMA acquisitions. CAL/VAL activities will be planned, whenever possible, in synergy with ESA (i.e. Fluorescence Explorer – FLEX and the candidate CHIME hyperspectral missions) and the actual ASI missions’ development (hyperspectral mission SHALOM).

The PRISMA acquisition plan is an opportunity to strengthen the ongoing collaborations between the Authors and the RADI Chinese colleagues in the framework of the active international collaborations programs (ESA Dragon-4 and CNR-CAS agreement). The opportunity to foster a synergy between the Italian PRISMA and the Chinese GF-5 missions, in order to increase the possibility to have more consistent hyperspectral time series suitable to monitor biophysical variables and agronomical processes.

This work aims to analyze the feasibility of application of multispectral imagery to detect anomalies caused by tree pests, towards an early warning monitoring system. The analysis take into account multispectral images from satellites as Sentinel-2 (10 mts. spatial resolution) and PlanetScope (3 mts. spatial resolution) imagery, and images obtained from sensors mounted on UAV (5 cms. spatial resolution), as MicaSense RedEdge sensor on board a UAV SR-SF6.

The scope of this study is the evaluation of this set of images to locate tree crowns, and: 1) distinguish between symptomatic and apparently healthy canopies, and 2) between symptomatic and affected asymptomatic trees.

The main pest of interest analyzed in this study is the Xylella fastidiosa (Xf), a vector-transmitted bacterial plant pathogen associated with serious diseases in a wide range of plants. Xf was detected on olive trees in Puglia, southern Italy, in October 2013, the first time the bacterium has been reported in the European Union. Since then it has also been reported as present in France, Spain and Germany. Controls are in place to prevent the bacterium from spreading.

In 2016, the European Food safety Authority (EFSA) concluded that research being carried out in Apulia showed that certain treatments reduce the symptoms of disease caused by Xf but do not eliminate the pathogen from infected plants (EFSA 2016). A significant difficulty that need to be taken into account for the containment of Xf, comes from its very wide host range (in September 2018 the list includes more than 500 plant species), and since infections that do not cause symptoms in some host–strain combinations, despite the infected hosts continuing to act as inoculum sources.

In (Zarco-Tejada et al. 2018) a multi-temporal trees inspection and airborne imaging data in several orchards has been carried out. The analysis found the physiological alterations caused by Xf infection at the pre-visual stage were detectable in functional plant traits assessed remotely by hyperspectral and thermal sensors. And it was confirmed the presence of Xf infection in the selected orchards by the quantitative Polymerase Chain Reaction (qPCR) assay.

The main area of interest for this study is region of Apulia, and as a reference, the current analysis took into account datasets and geo-locations of trees affected by this pest in olives orchards, identified during the field campaign carried out in (Zarco-Tejada et al. 2018). Furthermore, the study includes 3000 geo-locations of olive trees extracted from public records distributed by Apulia region, in cooperation with the Public Network of Research Laboratories (SELGE).

The framework to carry out the multi-temporal analysis with Sentinel-2 imagery was carried out in the Data Integration and Analysis System (DIAS). DIAS archive includes satellite data and ground observation data. These datasets are stored in an large volume disk array accessible via API requests.

The time lapse of the current analysis included Sentinel-2 images since 2016 until 2018, PlanetScope images for the period 2017-2018, and a UAV field campaign carried out in 2018.

As in (Zarco-Tejada et al. 2018), common multispectral vegetation indices, did not differ significantly between asymptomatic and symptomatic trees, so unable to detect non-visual symptoms of Xf infection. In the present study, despite it was carried out an identification and masking of background soil, some differences could be mainly associated to changes on soil background more than with tree anomalies, since spatial resolution of satellite imagery seems to be scarce to be used for a tree focused study but for an analysis at a parcel level, where at the same time the heterogeneity of trees status could differ.

Detailed analysis of multi-temporal vegetation indices profiles showed that remote sensing observations, can help to identify unexpected phenology patterns or anomalies that could be related to tree pests. Despite the capability detect individual trees vary according image spatial resolution, remote sensing techniques help to put into evidence a particular parcel where a further analysis need to focus.

In the big data era, various kinds of satellite data are increasingly made easily and/or freely available in the world. Therefore, the crop type mapping with these satellite data has strongly attracted the attention from the remote sensing researchers. As a new comer, the Chinese high resolution satellite series, short name for GF, are being developed in China. GF-1 data has a 16 meters ground sampling for 4 bands, such as blue, green, red and near infrared spectra. In Europe, the Copernicus project ensures the stable Sentinel satellite series and provides multispectral and 10-meter resolution optical satellite images to the worldwide end users. These satellite images become the rich data sources for the crop type mapping with the machine learning algorithm nowadays. In support of the provincial agricultural monitoring, we have developed an approach to use GF, Sentinel 2 and other third partner satellite images to mapping crop types in irrigation area of the yellow river of Ningxia, China. Field sample photos were taken with the GPS camera in summer 2017 and 2018 respectively and thereafter the crop types for the ground truth data were interpreted with a software, named GPS Photo Data Processor. With the support of these ground truth samples, more samples for the training and validation were further visually added over a clear sky image in key crop growth stage. The Random Forest was used as the classifier for this study as many literatures have reported that the RF algorithm overperformances other algorithms in many cases, such as SVM, Maximum Likelihood. The classification results of crop type map were evaluated with the error confusion matrix, in particular, OA(overall accuracy) and F1 Score. Sentinel 2A/B images during the growing season in 2017 and 2018 were collected and processed via the ESA Sent2Agri system that UCL developed. The GF satellite images were collected from CRESDA in China. All these data were further processed and finally made spatially congruent. The performance for crop type mapping with time series of each of these data sources was analyzed and compared. The results show that the accuracies were between 84-93%. The accuracy of crop type mapping with GF data was the lower due to less bands and other limitations. The accuracy of crop type mapping with all bands of Sentinel 2A/B reached the highest due to more key bands and higher resolution. The utilization of huge volume of the high resolution satellite images, such as Sentinel 2 is challenging to the researchers.

This abstract summarises the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m NDVI time series. The Artificial Neural Network (ANN) method is used where the output is a set of Area Fraction Images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classification are used to obtain training data for the ANN. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10x10 km2. For the five dominant classes in the test sites the R2 obtained after the aggregation are 89 % (winter cereals), 71% (grasslands), 78 % (sunflower), 92 % (broad-leaved forest), and 92 % (maize).

With the unique terroir, the region in the east hillside of Helan mountain in Ningxia is well recognized one of golden regions in the world for the cultivation of wine grape and the production of high quality wine, and thereafter this region was designated the protection area of national product of geographical indication in 2002. With the strong policy support, the vineyard develops very fast on the Gobi desert in recent years and the land use has changed obviously. This objective of this study is to monitor the evolution and changes of vineyard in the region with the Landsat8 data since 2013 and provide the scientific information for the decision maker of the vineyard management. Landsat8 data were downloaded from the USGS official website and formed the time series dataset after several step fine processes. The ground truth data were collected during 2016 to 2018. Based on the ground truth data in 2016 to 2018, with the reference map of high resolution of GOOGLE EARTH, the training samples for 2013 to 2015 were further obtained. The random forest was used as the classifier to have satellite images of each year classified. The results were validated with the error matrix and further verified with the field boundary data that was drawn by another group of researchers. The evolution and changes of the vineyard was further analyzed based on the validated results of vineyard map.

The pace of urbanization has been unprecedented. Today, 55 per cent of the world’s population live in cities and another 2.5 billion people is expected to move to urban areas by 2050 (UN, 2018). The UN 2030 Agenda for Sustainable Development gives a prominent role to monitoring the urbanization process. With its synoptic view and large area coverage at regular revisits, satellite remote sensing has been playing a crucial role in urbanization monitoring at regional and global scale. Several methodologies have been developed using Synthetic Aperture Radar (SAR) and/or multispectral imagery to map urban extent globally including the Global Urban Footprint (GUF) and the Global Human Settlement Layer (GHSL). These datasets provide a reliable global map of the urban areas, but they are characterized by low temporal resolution (i.e. every five years) which highlights the need of further research and method development

The objectives of this research are two folds, one is to develop a globally applicable and entirely automatic method to monitor urban footprints using Sentinel-1 SAR and Sentinel-2 MSI dense time series exploiting the Google Earth Engine (GEE) cloud platform, and other is to evaluate derived urban extent for the monitorin of the UN Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate. The innovative aspects of the developed method is to integrate Sentinel-1 and Sentinel-2 dense time series using a totally unsupervised approach. The estimation of the selected urban footprint is performed in several progressive steps. First, the area of interest is divided into mountainous and non-mountainous areas using an available DSM (i.e. SRTM or ALOS World 3D) to take into account the layover and foreshortening of SAR geometric distortions. Then, Sentinel-1 ascending and descending time series are processed in order to enhance the backscatter of stable urban areas and to compute the Sentinel-1 Urban mask using an automatic thresholding procedure. The latest step is to compute a probability urban map combining the Sentinel-1 Urban mask with the Sentinel-2 multi-spectral time series. All available Sentinel-2 images, acquired during the selected sensing period, will be used to compute a cloud-free Sentinel-2 image composite, subsequently we applied a segmentation algorithm to the Sentinel-2 composite, and for each object, we compute several multitemporal spectral indexes statistics (i.e. Min/Max NDVI, Median NDBI, Mean NDWI). Finally, we use a ruleset to estimate the probability urban map combining the Sentinel-1 Urban mask with the computed Sentinel-2 indexes statistics.

To ensure its global applicability, we tested the developed approach in several cities worldwide (i.e. Beijing, Lagos, Milan, Mumbai, New York, Rio and Stockholm) characterized by different urban density and morphologies. We computed the urban footprint in different periods to evaluate the temporal stability of the method and to produce urban footprint time series. The results show that through this method it is possible to obtain high accuracy (kappa higher than 0.85) with respect to the reference data acquired within the EO4Urban project in all cities and in different periods ^[5]. The developed method obtains equal or higher accuracy than GUF and GHSL data in the same area, and a visual comparison shows that the integration of the Sentinel-1 SAR and Sentinel-2 MSI data leads to achieving highly detailed information.

Based on the methodology defined by the UN Habitat, the urban extraction results are being used in the monitoring of Urban SDG indicator 11.3.1 Ratio of land consumption rate to population growth rate in the selected cities. The preliminary results show that timely and reliable urban extraction is essential for the definition of cities and for monitoring Urban SDG indicator 11.3.1.

In the last ten years, the harmonization of rapid urbanization and extremely prosperous economic activity with the air quality and urban land use is the most concerned issue in Chinese urban development policy [1][2]. Accordingly, an urgent and challenging task is to improve the knowledge and understanding of change patterns in human settlements for fast-urbanized megalopolis in South and East Asia. Thanks to the availability of time-series of heterogeneous remote sensing data, it is now possible to explore these changes decoupling those due to urban expansion and those due to increasing economic activities [3]. In this work, we combine multi-temporal the sentinel-1 SAR C-band sensor and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime sensor (also called the Day/Night Band, or DNB) to explore urban change patterns at the geographical scale of Chinese Megalopolis. The joint use of heterogeneous sensor allows discovering more spatial-temporal features and deeper relationships between urban construction and nighttime-based changes, which indirectly reflect the connections between urbanization and economic development.

Three megalopolis, namely the Jingjinji, the Yangtze River Delta and the Pearl River Delta have been selected, which correspond to the currently most developed and the most densely populated portions in P.R.China. First, Sentinel-1 SAR is used to extract urban extents that ensure the focus of our analysis is in built-up areas at the finest spatial resolution for freely available data sets. To handle big-size data over each Megalopolis, defined as a chain of roughly adjacent metropolitan areas, which may be somewhat separated or may merge into a continuous urban region, the critical preprocessing steps and computations are performed in Google Earth Engine (GEE). Then, data-driven unsupervised classification is used to explore change patterns according to a feature space joining the base and the change images. In this way, both the initial state and the temporal change pattern are considered. To ensure the reliability of unsupervised clustering, GMM, K-method, and DBSCAN are adaptively applied to the same feature space. At last, the 2-dimensional vector analysis are given to interpret the clustering results. Consider the resolution difference between the Nighttime light sensor and Sentinel-1 SAR, upscaling is applied to the SAR images to match VIIRS images at 500-meter resolution.

The vector analysis according to the extracted clusters shows that these clusters are interpretable as meaningful temporal and spatial patterns, although the interpretability of the extracted cluster patterns depends on the classifier performance to different feature spaces. According to the most stable clustering results, change patterns of urban construction and nighttime fundamental facilities are clearly differentiated between core urban and suburban areas, and very new development city zone are singled out and highlighted.ß

Ref:

[1] Wang, Shuxiao, et al. "Effectiveness of national air pollution control policies on the air quality in metropolitan areas of China." Journal of Environmental Sciences 26.1 (2014): 13-22.

[2] Wu, Yanyan, Shuyuan Li, and Shixiao Yu. "Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China." Environmental monitoring and assessment 188.1 (2016): 54.

[3] Frolking, Steve, et al. "A global fingerprint of macro-scale changes in urban structure from 1999 to 2009." Environmental Research Letters 8.2 (2013): 024004.

Urban areas have developed mainly against a socio-economic paradigm ignoring to a large extent the environmental impacts Of particular concern for cities in our days, is the lack of balance between the natural, built and socio-economic environments, leading among others, to the degradation of their thermal environment, in particular overheating. Heat islands/spots within the urban ecosystems have negative impact to human health especially for vulnerable groups, increase energy use for cooling and lead to poor city energy efficiency, intensify energy poverty, deteriorate air quality and result in socio-economic problems in general.

While in vegetated areas, evaportranspiration transfers most of the incoming radiation into latent heat, in built up areas sensible heat is generated, which leads to the strong heat load of urban areas. In addition buildings strongly affect the flow patterns of wind and heat, practically keeping the heat close to the ground.

Mitigation plans to counteract overheating are now developed by several cities around the world, in line to the Sustainable Development Goals of the United Nations and the new Covenant of Mayors for Climate and Energy, which recognizes the role of ecosystem-based mitigation in enhancing urban resilience and providing multiple benefits.

Plans also take advantage of Nature Based Solutions (NBS) in an effort to restore the urban ecosystems and achieve the needed balance between the natural, the built and the socio-economic environments.

The scope of this paper is to assess the factors in support of a Planning Support System (PSS) for the design of the appropriate, science and policy wise, NBS so as to restore urban ecosystems, counteract overheating and improve thermal resilience.

Regional land subsidence is an integrated systematic issue related to multidisciplinary and being of global focus, and has been being a serious threat to the urban infrastructure, high-speed railway and the utilization of underground space, and restricting the sustainable development of society. The study of the regional subsidence evolution in Beijing Plain is of great significance: it is necessary to reveal the regional land subsidence evolution pattern under the background of Integration of Beijing-Tianjin-Hebei and the South-to-North Water Diversion. Furthermore, it can help to realize the scientific regulation of regional subsidence and ensure the sustainable development of regional economy and society, which has a special significance and application prospect. Therefore the MT-InSAR method is used to obtain the regional ground subsidence time series information of the study area in three periods: Jun. 2003 ~ Aug. 2010, Oct. 2010 ~ Nov. 2015, and May. 2015 ~ Jun. 2018. Then equations are established based on the time-overlapping information to complete the fusion of multi-platform time series, the inconsistence between different reference points is solved, simultaneously. The results show that, the maximum subsidence values in Beijing Plain are 690.6 mm, 649.2 mm and 411.7 mm during the three periods, with maximum deformation rates of 100.6 mm/a, 130.0 mm/a and 142.3 mm/a, respectively. For the spatial distribution and the evolution of the land subsidence field, the weighted spatial kernel density analysis, profile analysis, trend-surface analysis and profile-gradient analysis are used to analyze the spatial-temporal evolution characteristics of the land subsidence field. In this case, land subsidence in Beijing Plain are thoroughly analyzed overall distribution characteristics and evolution process. Nine subsidence centers are identified and the subsidence centers are connecting to form a main subsidence area in the northern part of Beijing Plain. The spatial clustering degree of the subsidence in the Beijing Plain indicates an overall heterogeneity in spatial. Moreover, the northern subsidence areas spread along the Nankou-Sunhe fault, and is cut into several subsidence centers by active faults, indicating that the regional geological structure has obvious control effect on the spatial distribution of land subsidence areas. The evolution of the subsidence field: the northern subsidence areas spread along the northwest-southeast direction, and then expands to both the east and west sides. Then through the distribution of subsidence areas and groundwater funnel, the InSAR based time series and the monitoring well based groundwater level changes, the correlations in spatial and responses between land subsidence field and groundwater flow field are analyzed. The results show that the subsidence center in the northern Beijing Plain is consistent with the groundwater drop funnel in spatial, with a similar downward trend over the whole observation time. Through the analysis of well based results located in different areas, the long term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction; while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics as the groundwater level changes.

Cities worldwide experience enhanced heat stress, as a result of the impact of their surface properties and geometry on the surface energy balance. Land surface temperature (LST) from satellite thermal imagery can provide an overall view of extended urban areas, assisting in the identification of thermally vulnerable sites for mitigation responses. Several studies have demonstrated the relationship between LST and vegetation fraction within cities. Here, the Sentinel-3 SLSTR Level-2 LST product is used to examine the synergy between several two- and three-dimensional properties of the urban morphology (e.g. building height, canyon aspect ratio) and LST, for summer conditions. Results, demonstrate that at daytime open impervious urban areas experience higher temperatures than densely built neighborhoods. The contrasting thermal patterns of nighttime imagery reveal the surface heat island development cycle, and indicate the potential caveat of using solely LST as a map based planning tool, due to its dependence on satellite overpass timing. Furthermore, the surface temperature distribution is examined in the context of Local Climate Zones (LCZs); in general, a considerable differentiation among LCZs is found, although local circulations exhibit a stronger control on coastal zones

Benefit from steering antenna beam, TOPS (Terrain Observation by Progressive Scans), the default imaging model of Sentinel-1, can obtain wide coverage with azimuth-invariant SNR and at the same time avoid scalloping. Meanwhile, the introduced Doppler centroid difference higher than 5000 Hz requires a stringent co-registration accuracy to prevent phase discontinuities over burst boundaries. For multi-temporal analysis, it is even harder to achieve the required accuracy. The residual mis-registration can bias the extracted geophysical inversion parameters such as surface deformation. To improve the co-registration accuracy, a co-registration approach based on Floyd-Warshall algorithm and Enhanced Spectral Diversity is proposed. For further improvement in low coherence regions, we present a coherence estimator by combining two consecutive bursts SLC samples. The performance of the presented approach is validated by two low coherence scenes.

PolSAR data has become the significant data source in urban research. However, the widely used methods extract features at the expense of spatial resolution loss. A PolSAR feature optimization approach is addressed. The new method relies on the adaptive selection of homogeneous samples, both polarimetric and spectral characteristics are taken into account. Those homogeneous samples are first applied to suppress the speckle and to refine the feature estimators afterwards. The comparison with the state-of-the-arts using real-world datasets shows that more accurate PolSAR features with well-preserved edges can be obtained over textural regions.

Urbanization is one of the main factors to cause the land use change in the world, the urban area is rapidly expanding with the accelerated process of urbanization, so the knowledge of urban distribution can provide a reliable technical and decision-making basis for urban planning and development potential evaluation. Remote sensing technology has played an active role in the extraction of urban land, and the urban light at night can be used to analyze the city expansion trend and development potential through light intensity and change rate. In this study, cities in Southeast Asia were taken as the research objects, and landsat8 OLI remote sensing data was taken as the main data source. Firstly, spectral characteristics of different objects were analyzed, and the thresholds of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were determined to remove the vegetation and water. The difference between the remaining objects were enlarged after the removal of vegetation and water, the normalized difference building index (NDBI) and the minimum distance classification method were adopted to eliminate the unplanted farmland and bare land. The results showed that this method was simple and capable to extract the impervious land step by step and the accuracy was above 85%. The slope of light change was calculated based on the light data of Defense Meteorological Satellite Program (DSMP) between 2000-2013, in order to analyze the development trend and potential of urban and its surrounding area. The results showed that the urban light index increased at an average annual rate of 0.75 in the built-up area and the surrounding area grew at a high rate of 1.03 per year in the past years from 2000 to 2013, which showed a high density and quick growth rate. It still maintained a strong growth trend and had great potential and space for development.

Over the past two decades, there has been substantial urban growth in Stockholm, Sweden. As a result of accelerating urbanization, Stockholm is now the fastest-growing capital in Europe and it is expected that the Greater Stockholm area will more than double in population by 2045, to 4.5 - 5 million people. The Swedish government has recently taken steps to ensure sustainable management of its green and blue resources in urban areas by requiring all counties to draw up regional plans for their green infrastructure. Using Sentinel-2 and SPOT 5 images, this research investigates the evolution of land cover change in Stockholm County between 2005 and 2015 with a particular focus on what impact urban growth has had on protected green areas, green infrastructure and urban ecosystem service provision.

One scene of Sentinel-2A MSI imagery from 2015 and ten scenes of SPOT 5 imagery from 2005 over Stockholm County were selected for this study. These images are classified into 10 land cover categories using an object-based SVM classifier with spectral, shape and texture features as inputs. The classifications are then used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2005 and 2015, including generation of land cover change statistics, urban ecosystem service provision bundles which include spatial configuration information and evaluation of impact on legislatively protected areas as well as ecologically important habitat networks.

Preliminary results indicate that Urban areas increased by15% or approximately 116 km² while non-urban land cover, mainly agricultural areas and green structure, decreased by just under 4%. The increase in urban areas is just over 2% of the total county land area. More specifically, the results suggest that urban areas may soon overtake agricultural areas to become the second largest land use/cover category in the county landscape after forest. The largest increases in urban areas and significant losses of green structure occurred mainly in the northern and southern outskirts of the county in the rural-urban fringe, with the exception of two municipalities close to Stockholm city which also experienced significant urban growth.

In terms of ecosystem service provision, notable decreases occurred in temperature and global climate regulation, air purification, noise reduction and recreation, place values and social cohesion. Urban areas within a 200m buffer zone around the Swedish EPA’s nature reserves in Stockholm County increased by 16% over the decade, with several examples of new urban areas constructed along the boundary of nature reserves. Further research will include evaluation of important ecological networks in Stockholm county, such as its regional green wedges and broadleaved forest distribution, to see how these have been affected by the urban growth. The results of this study can assist policymakers and planners in their efforts to ensure sustainable urban development and natural resource management for the Stockholm region.

Over the past several decades, the unprecedented pace of urbanization and socioeconomic development in China has placed great anthropogenic pressures on inland surface water quality. In recent years, continuously increasing environmental investments have been undertaken to control pollutant discharge and improve inland water quality across the entire country. However, the quantitative response of water quality to both increasing human pressure and effort is less well understood, particularly for a large-scale region with diverse driving factors. In this study, we use satellite-derived nocturnal radiance signals as proxy measures for both the negative (using the product of lit area and population size) and positive (using the area-weighted magnitude of nighttime lights) effects of human activity to evaluate how water quality changes over time in response to anthropogenic disturbances. Our method clearly demonstrates the extended application of remotely sensed data of nighttime lights with dual actions, particularly in the absence of direct observations of socioeconomic variables due to their consistent, timely and spatially explicit proxy measures for diverse human activities.