Earth Sensor Research Articles

Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial carbon cycling and global sustainable development goals (SDGs). With complex spatial distribution, composition and ecological functions, UV is essential for global carbon cycling and climate change. Therefore, improving UV carbon storage capacity modeling is a research hotspot that deserves extensive investigation. However, the uniqueness of UV lead to great challenges in carbon storage modeling, including (1) limitations in data and algorithms due to complex and sensitive urban environments; (2) the severe scarcity of in-city field observation data (e.g., EC towers and field surveys); (3) difficulty in parameter inversion (e.g., canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural vegetation to urban scenarios. The progress in carbon storage modeling in urban settings is reviewed, with detailed discussions on carbon storage modeling methods and major challenges. We then propose strategies to overcome existing challenges, including (1) implementing novel and improved remote sensing (RS) techniques (e.g., hyper-spectral, LiDAR, carbon satellites, etc.) to obtain enhanced structural and functional information on UV; (2) improving critical nodes of the earth observation sensor network, especially the distribution of EC towers in urban settings; (3) leveraging “Model-Data Fusion” technology by integrating big earth data with carbon estimation models to reduce the uncertainty in UV carbon storage estimations. This review provides new insights for modeling UV carbon storage and is expected to help the research community to achieve a better understanding of UV towards carbon neutrality.

New Generation Hyperspectral Sensors DESIS and PRISMA Provide Improved Agricultural Crop Classifications

Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the Earth. Therefore, our overarching goal in this study was to use these data to explore advances that can be made in agricultural research. We selected PRISMA and DESIS images during the 2020 growing season in California's Central Valley to study seven major crops. PRISMA and DESIS images were highly correlated (R 2of 0.9–0.95). Out of the 235 DESIS bands (400–1000 nm) and 238 PRISMA bands (400–2500 nm), 26 (11%) and 45 (19%) bands, respectively, were optimal to study agricultural crops. These optimal bands provided crop type classification accuracies of 83–90%. Hyperspectral vegetation indices to estimate plant pigment content, stress, biomass, moisture, and cellulose/lignin content were also identified.

Algorithm for determining satellite orientation based on use of earth sensor and magnetometer

An algorithm for determining the orientation of a satellite based on information from an Earth sensor and a magnetometer is considered. The effect of instrumental errors of the magnetometer on the accuracy of orientation determination is analyzed. The TRIAD algorithm is compared with an algorithm in which only the heading angle is calculated.

Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach

Toxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today’s world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data.

EVALUATING APPROACHES RELATING ECOSYSTEM PRODUCTIVITY WITH DESIS SPECTRAL INFORMATION

Abstract. Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP.

TARGET DETECTION USING DLR EARTH SENSING IMAGING SPECTROMETER (DESIS) DATA

Abstract. DLR’s Earth Sensing Imaging Spectrometer (DESIS) is mounted on the International Space Station (ISS). DESIS records data in the spectral range from 400 to 1000 nm with a spectral and spatial resolution of 2.55 nm and 30 m respectively. The high spectral resolution enables in detecting a target object distinctly in remotely sensed imagery which has many useful applications in different fields of surveillance and monitoring. In present work two different case studies have been carried out that use DESIS data for target detection. In the first case study brick kilns are detected in DESIS data using Adaptive Coherence Estimator (ACE) algorithm. In the second case study Photovoltaic (PV) panels are considered as target object and linear spectral unmixing is employed to distinctly detect them in the image. From experimental results it is observed that the first target which were sparsely located in the image is detected very precisely with F1 score value of 0.97. The accuracy of the output of PV panel detection is observed to be more than 98%. Both the case studies show the potential of DESIS data in target detection which is a very important application of hyperspectral remote sensing.

VNIR HYPERSPECTRAL ANALYSIS OF RAMGARH ASTROBLEME IN NORTHERN INDIA

Abstract. Hyperspectral remote sensing has immense potential to characterize any surface by utilizing the detailed spectral information obtained from hundreds of narrow and contiguous bands. This work utilizes the latest DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral data from the Ramgarh astrobleme (crater) in the state of Rajasthan, India to identify the surface geology and the changes that have undergone. Vertex Component analysis has been applied on DESIS data, and the output end members were compared with the reference spectra from the spectral library. However, due to the amount of vegetation in the study area, the Vegetation Response Removal (EVRR) thresholding model is proposed to remove the effect of vegetation. With the application of EVRR, the presence of quartz with impurities is noticed.

USING DESIS AND EO-1 HYPERION REFLECTANCE TIME SERIES FOR THE ASSESSMENT OF VEGETATION TRAITS AND GROSS PRIMARY PRODUCTION (GPP)

Abstract. This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400–2400 nm) reflectance collection over vegetated flux sites, to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using together VNIR reflectance from EO-1 Hyperion and DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.

THE DESIS L2A PROCESSOR AND VALIDATION OF L2A PRODUCTS USING AERONET AND RADCALNET DATA

Abstract. The hyperspectral instrument ”DLR Earth Sensing Imaging Spectrometer” (DESIS) is a VNIR sensor on-board of the International Space Station (ISS) and operational since October 2019. DESIS acquires images of Earth on user request with a swath of about 30 km width and 235 bands with a Full Width at Half Maximum (FWHM) of 3.5 nm in the spectral range between 400 to 1000 nm.In this article we will present the basis of the atmospheric correction by PACO software, implemented inside the DESIS Ground Segment as L2A processor. The resulting L2A products will be validated against independent in-situ measurements.The aerosol optical thickness and water vapor will be compared with the Aerosol Robotic Network (AERONET) measurements and the surface reflectance will be validated with the Radiometric Calibration Network (RadCalNet) data.

Automatic air pluvation system for physical modelling applications

This paper presents an air pluviation system, developed to facilitate 1g physical model tests in granular soils. The deposition process is fully automated and requires minimal input from the operator, thereby significantly reducing the time required to deposit large volumes of granular material, improving the uniformity of the prepared specimens and the reliability of test results. The components comprising the pluviation system have been calibrated to produce loose-to-very dense sand beds, of relative density that ranges between Dr = 7% and > 100% of the maximum density achieved with the procedures described in the pertinent standards. The testing chamber where sand is deposited is instrumented with an array of pressure sensors, and the rig is equipped with a miniature cone penetration testing (mini-CPT) device. Measurements from the earth pressure sensors and cone tip resistance profiles are used to evaluate how friction at the sand–chamber interfaces affects the distribution of geostatic stresses inside the chamber, the uniformity of sand beds and boundary effects during deposition and during mini-CPT testing. The air pluviation system allows preparing layered sand profiles by adjusting the deposition parameters on the fly, and this feature is demonstrated through the analysis of mini-CPT tests performed in layered sand beds.

A Crossmodal Multiscale Fusion Network for Semantic Segmentation of Remote Sensing Data

Driven by the rapid development of Earth observation sensors, semantic segmentation using multimodal fusion of remote sensing data has drawn substantial research attention in recent years. However, existing multimodal fusion methods based on convolutional neural networks cannot capture long-range dependencies across multiscale feature maps of remote sensing data in different modalities. To circumvent this problem, this work proposes a crossmodal multiscale fusion network (CMFNet) by exploiting the transformer architecture. In contrast to the conventional early, late, or hybrid fusion networks, the proposed CMFNet fuses information of different modalities at multiple scales using the cross-attention mechanism. More specifically, the CMFNet utilizes a novel cross-modal attention architecture to fuse multiscale convolutional feature maps of optical remote sensing images and digital surface model data through a crossmodal multiscale transformer (CMTrans) and a multiscale context augmented transformer (MCATrans). The CMTrans can effectively model long-range dependencies across multiscale feature maps derived from multimodal data, while the MCATrans can learn discriminative integrated representations for semantic segmentation. Extensive experiments on two large-scale fine-resolution remote sensing datasets, namely ISPRS Vaihingen and Potsdam, confirm the excellent performance of the proposed CMFNet as compared to other multimodal fusion methods.

SemiSegSAR: A Semi-Supervised Segmentation Algorithm for Ship SAR Images

Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly by using large amount of labeled data. Yet, the high cost related to the samples labeling and their scarcity result in significant limitation of their wide use. Therefore, it is crucial to overcome the unlabeled inputs challenge and develop semi-supervised learning approaches to enhance the machine learning models capacity. Our letter proposes a semi-supervised segmentation algorithm for SAR images named SemiSegSAR based on the use of Graph Signal Processing. This method includes instance segmentation; texture and statistical SAR features to represent the nodes of the graph; K-nearest neighbors to construct the graph; and Sobolev minimization algorithm to tackle the problem of semi-supervised semantic segmentation. The proposed algorithm is trained and tested using the publicly available SSDD and HRSID ship detection datasets. Experiments show that SemiSegSAR outperforms the current state-of-the-art semi-supervised and supervised methods while requiring only few labeled data.

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luft - und Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400-1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have 4 multispectral broadbands (MBB) with 3-4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72-100% and 75-100% respectively for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56-100% and 61-100% respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65-100% and 60-94% respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Simulink-based simulation platform design and faults impact analysis of attitude control systems

AbstractThe satellite attitude control system (SACS) is a complicated system. In order to reflect the relationship among different components in SACS and analyse the impact of component faults on system performance, a complete simulation platform of the SACS based on Simulink is built in this paper. With the embedding of the specific reaction flywheel, gyroscope and earth sensor model, and the design of the controller based on the quaternion feedback, the simulation platform can not only simulate the real SACS at the component level, but it can also realise the injection of component faults for analysing the system performance. Simulations are conducted to verify the performance of the simulation platform. Simulation results show that this simulation platform has the ability to accurately reflect the control performance of the SACS, and the output accuracy of the component model is high. The research results reveal that this simulation platform can provide model support for verifying the algorithm of fault diagnosis, prediction and tolerant control of the SACS. This simulation platform is easy to use and can be expanded and improved.

Intelligent Sensor Fusion in High Precision Satellite Attitude Estimation Utilizing an Adaptive-Network-Based Fuzzy Inference System

In this study, Adaptive Network-Based Fuzzy Inference System (ANFIS) is presented with sensor data fusion approach to estimate satellite attitude. The active sensors are sun and earth sensors. Satellite attitude dynamic, including attitude quaternion and angular velocities are estimated simultaneously utilizing the measured values by the sensors. The Extended Kalman Filter (EKF) is employed to verify and evaluate the efficiency of the presented method. Additionally, the neural networks with Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) are also designed to prove the superiority of the proposed ANFIS network among the smart methods of sensor data fusion for satellite attitude estimation. Root Mean Square Error (RMSE) as a numerical criterion and graphical analysis of residues are utilized to evaluate the simulation results. The simulations confirm that the obtained estimations from ANFIS network have more accuracy in modeling of nonlinear complex systems compared to EKF, MLP and RBF networks. In general, using intelligent data fusion, especially ANFIS, reduces attitude estimation error and time in comparison to the classical EKF method.

Undrained compressibility characteristics and pore pressure calculation model of foam-conditioned sand

Soil conditioning with foam is widely used to transform natural soil into the desired state suitable for mechanised tunnelling. Since the excavated soil is rapidly discharged out with little water drained during earth pressure balance (EPB) shield tunnelling, it is necessary to investigate the undrained compressibility characteristics of foam-conditioned soil under chamber pressure. In this study, a series of compression tests were carried out in a large-scale, self-designed compression device equipped with pore pressure and lateral earth pressure sensors. The results demonstrated that the foam considerably enhanced the compressibility, pore pressure and lateral pressure of the conditioned sand. The workability of foam-conditioned sand may be well conducted in slump tests under the atmospheric condition, but the compressibility of foam-conditioned probably cannot satisfy its requirement for EPB shield tunnelling due to compression of foam under high chamber pressure. In addition, the total and effective lateral pressure coefficients of the foam-conditioned sand increased and decreased, respectively with increasing foam injection ratio and total vertical pressure. Then, the mechanism of the compressibility of foam-conditioned sand is explained with an existing two-dimensional (2-D) model. Finally, according to Boyle’s law, an analytical model is proposed to predict the pore pressure of the foam-conditioned sand under compression pressure, and a satisfactory agreement between the calculated values and the test results was found.

Rao-Blackwellized Particle Filter for the CBERS-4 attitude and gyros bias estimation

The Rao-Blackwellized Particle Filter (RaoBPF) and the Unscented Kalman Filter (UKF) were applied in this work to attitude and gyros bias estimation using simulated orbit and attitude measurement data for CBERS-4 (China Brazil Earth Resources Satellite 4) recently in operation. CBERS-4 was launched in 2014, controlled and operated in shifts by China (Xi’an Control Center) and Brazil (Satellite Control Center). Its orbit is heliosynchronous with an inclination of 98.504 degrees, a semi-major axis of 7148.865 km, eccentricity 1.1×10−3, crossing equador line at 10h30min in a descending direction with perigee frozen at 90 degrees, which establishes a commitment relationship between a satisfactory amount of solar irradiance, contrast between targets, and the presence of clouds. This configuration provides global coverage every 26 days. The real orbit and attitude measurements were provided by the Satellite Control Center of the National Institute for Space Research (CCS - INPE) from September 1st, 2015. The dynamic attitude model is described by quaternions. The available attitude sensors are two Digital Sun Sensors (DSS), two Infrared Earth Sensor (IRES) and a triad of mechanical gyroscopes. The two IRES give direct measurements of roll and pitch angles with a certain level of error. The two DSS are nonlinear functions of roll, pitch, and yaw attitude angles. The gyros furnish the angular measurements in the body frame reference system. Gyros provide direct incremental angles or angular velocities; however, they present several sources of error, and the drift is the most troublesome. Such drifts yield along time an accumulation of errors which must be accounted in the attitude determination process. The RaoBPF estimation method used to attitude and gyros bias estimation is a technique that exploits the state space structure in order to reduce the number of particles, decreasing the processing time, avoiding the computational effort common to the standard particle filter. The logical extension of the RaoBPF provides a more general model that can be divided into purely non-linear and conditionally linear-Gaussian aspects, which explores this structure, marginalizing the conditional linear parts and estimating them using exact filters, such as the Extended Kalman Filter (EKF). The results show that it is possible to achieve precision in determining attitudes within the prescribed requirements using the RaoBPF, with lower computational cost when compared to the standard particle filter and its branches, in addition to have competitive results such as the UKF.

DLR Earth Sensing Imaging Spectrometer (DESIS) Level 1 Product Evaluation Using RadCalNet Measurements

The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 and integrated into MUSES. DESIS measures energy in the spectral range of 400 to 1000 nm with high spatial and spectral resolution: 30 m and 2.55 nm, respectively. DESIS data should be sufficiently quantitative and accurate to use it for different applications and research. This work performs a radiometric evaluation of DESIS Level 1 product (Top of Atmosphere (TOA) reflectance) by comparing it with coincident Radiometric Calibration Network (RadCalNet) measurements at Railroad Valley Playa (RVUS), Gobabeb (GONA), and La Crau (LCFR). RVUS, GONA, and LCFR offer 4, 15, and 5 coincident datasets between DESIS and RadCalNet measurements, respectively. The results show an agreement between DESIS and RadCalNet TOA reflectance within ~5% for most spectral regions. However, there is an additional ~5% disagreement across the wavelengths affected by water vapor absorption and atmospheric scattering. Among the three RadCalNet sites, RVUS and GONA show a similar measurement disagreement with DESIS of ~5%, while LCFR differs by ~10%. Agreement between DESIS and RadCalNet measurements is variable across all three sites, likely due to surface type differences. DESIS and RadCalNet agreement show a precision of ~2.5%, 4%, and 7% at RVUS, GONA, and LCFR, respectively. RVUS and GONA, which have a similar surface type, sand, have a similar level of radiometric accuracy and precision, whereas LCFR, which consists of sparse vegetation, has lower accuracy and precision. The observed precision of DESIS Level 1 products from all the sites, especially LCFR, can be improved with a better Bidirectional Reflection Distribution Function (BRDF) characterization of the RadCalNet sites.

UNESCO World Heritage properties in changing and dynamic environments: change detection methods using optical and radar satellite data

The article presents recent capabilities of active and passive earth observation sensors along with related processing image chains, for monitoring UNESCO World Heritage properties. Exceptional heritage sites and landscapes are found in dynamic environments, whereas both anthropogenic and natural changes are observed. The use of radar and optical satellite imageries can be used as a systematic observation tool for stakeholders, to map drastic or slowly driven landscape changes towards the better protection and management of these sites and their surrounding areas. The study presents the results from the analysis of the European Copernicus Sentinel-1 and Sentinel-2 satellite images over two broader areas in the Eastern Mediterranean basin that hold important UNESCO World Heritage properties. Initially, a recent strong earthquake of a 6.7 magnitude scale in the Aegean Sea is studied using radar Sentinel-1 images. These radar images were processed through the Hybrid Pluggable Processing Pipeline (HyP3) cloud platform for analyzing both significant changes of the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) backscattering signal as well as through an Interferometric Synthetic Aperture Radar (InSAR) analysis. Then, long-term changes in Cyprus during the last two decades are monitored by a Sentinel-2 image compared to the European Corine Land Use Land Cover data of 2000. These changes are mapped after a supervised classification process using the random forest (RF) classifier. The overall results demonstrate that the recent developments of the space sector in all its segments (resolution of the sensors, the capacity to storage in the cloud, processing advancements and open-access datasets and tools) can be beneficial for monitoring UNESCO World Heritage properties.