High-resolution Satellite Imagery Research Articles

Using a Novel Green Index to Support Ecosystem Services in a Megacity

We present a novel and efficient approach that enables the evaluation of environmental quality in cities worldwide using high-resolution satellite imagery, based on a new green index (GI) through multivariate analysis, to compare the proportion of urban green spaces (UGSs) with built and impervious surfaces. High-resolution images were used to perform a supervised classification of 25 districts in the city of São Paulo, Brazil. Only 11 districts showed higher urban forests, green spaces, green index, and green vs. built values, and impervious surface proportions with lower impervious and built spaces. On the other hand, the remaining districts had higher population densities and unfavorable conditions for urban ecosystem development. In some cases, urban green spaces were three-times smaller than the built and impervious surfaces, and none of the districts attained a high green quality index (0.75 to 1). Artificial intelligence techniques improved the precise identification of land cover, particularly vegetation, such as trees, shrubs, and grasses. The development of a novel green index, using multivariate statistical analysis, enhanced positive interactions among soil cover classes, emphasizing priority areas for enhancing environmental quality. Most of them should be prioritized by decision makers due to the low environmental quality, as identified by the low green index and worse ecosystem services, well-being, and health outcomes. The method can be employed in many other cities to enhance urban ecosystem quality, well-being, and health. The green index and supervised classification can characterize pastures, degraded forest fragments, and guide forest restoration techniques in diverse landscapes.

Spatial variability of slope movements in central and western Nepal Himalaya: Evaluating large-scale landslides to cut-slopes

Spatial variability of slope movements is common in the Himalayan terrain of central and western Nepal Himalaya due to the intricate topography, differential geo-environment, and frequent rainstorms. The process of mass movements in the Himalaya has been described in the past by many researchers. The spatial variability of slope movement phenomena is scale dependent and is affected by causative and triggering processes. The limited researchers delivered output to understand the scale-dependent spatial variability resulting from the causative and triggering mechanism of slope movement phenomena. This study has presented a rigorous scientific examination of the spatial variability of slope movements, focusing on large-scale landslides to cut slopes across the central and western regions of the Nepal Himalaya. To achieve this, a multidisciplinary approach was employed, encompassing geospatial analysis, remote sensing, and field investigations. High-resolution satellite imagery was utilized to identify and map slope movement features, while digital terrain analysis techniques aided in quantifying their characteristics. This approach quantitatively analyzed slope movement distribution, frequency, and characteristics in terms of various geo-environmental settings. The findings reveal diverse patterns of slope movements influenced by complex interactions between geological factors, geomorphology, triggering factors, and anthropogenic activities. Geological and geomorphological heterogeneity play roles differently in the spatial distribution of slope movements. Moreover, rainfall distribution and peak ground acceleration act similarly for the scale dependency phenomena of slope movement. The spatial variation concerning the causative and triggering variables signify the scale-dependency nature of slope movement processes. This study has provided insights into the scale dependency and spatially variable nature of slope movement events due to variations in causative and triggering mechanisms in the Nepal Himalaya.

SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection

Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing meteorological satellite imagery. Akin to various vision tasks, ImageNet pre-training is commonly used for pre-training SFD. However, in the context of multi-spectral meteorological satellite imagery, the initial step of deep learning has received limited attention. Recently, pre-training with Very High-Resolution (VHR) satellite imagery has gained increased popularity in remote-sensing vision tasks, showing the potential to replace ImageNet pre-training. However, it is worth noting that the meteorological satellite imagery applied in SFD, despite being an application of computer vision in remote sensing, differs greatly from VHR satellite imagery. To address the limitation of pre-training for SFD, this paper introduces a novel deep-learning paradigm to the meteorological domain driven by Masked Image Modeling (MIM). Our research reveals two key insights: (1) Pre-training with meteorological satellite imagery yields superior SFD performance compared to pre-training with nature imagery and VHR satellite imagery. (2) Incorporating the architectural characteristics of SFD models into a vanilla masked autoencoder (MAE) can augment the effectiveness of meteorological pre-training. To facilitate this research, we curate a pre-training dataset comprising 514,655 temporal multi-spectral meteorological satellite images, covering the Bohai Sea and Yellow Sea regions, which have the most sea fog occurrence. The longitude ranges from 115.00E to 128.75E, and the latitude ranges from 27.60N to 41.35N. Moreover, we introduce SeaMAE, a novel MAE that utilizes a Vision Transformer as the encoder and a convolutional hierarchical decoder, to learn meteorological representations. SeaMAE is pre-trained on this dataset and fine-tuned for SFD, resulting in state-of-the-art performance. For instance, using the ViT-Base as the backbone, SeaMAE pre-training which achieves 64.18% surpasses from-scratch learning, natural imagery pre-training, and VRH satellite imagery pre-training by 5.53%, 2.49%, and 2.21%, respectively, in terms of Intersection over Union of SFD.

Bottom-up assessment of household electricity consumption in dynamic cities of the Global South—Evidence from Kigali, Rwanda

Data on electricity consumption is crucial for assessing and modeling energy systems, making it a key element of sustainable urban planning. However, many countries in the Global South struggle with a shortage of statistically valid, geocoded, and disaggregated household-level data. This paper aims to develop a generic methodology for the generation of such a database in terms of electricity consumption. The methodology was tested in Kigali, the capital city of Rwanda, with a focus on all single-family residential building types of the inner city. Discrete data on buildings is obtained through combined information products derived from very high resolution (VHR) satellite imagery, field surveys, and computer assisted personal interviewing. In total, 509 valid geocoded survey datasets were used to evaluate and model household electricity consumption, as well as electrical appliance ownership. The study's findings reveal that the arithmetic mean of specific electricity consumption was 3.66 kWh per household per day and 345 kWh per capita per year in 2015. By subdividing the data into distinct building types as well as their spatial location, and weighting the specific values according to their proportion in the study area, a more accurate mean value of 1.88 kWh per household per day and 160 kWh per capita per year was obtained. Applying this weighted mean to extrapolate household electricity consumption for the study area, in conjunction with the sample's precision level, resulted in an estimate of 126–137 GWh for the year 2015. In contrast, using the arithmetic mean would have led to values twice as high, even exceeding the total electricity consumption of the entire city, including multi-family and non-residential buildings. The study highlights the significance of on-site data collection combined with geospatial mapping techniques in enhancing of understanding of residential energy systems. Using building types as indicators to distinguish between households with contrasting electricity consumption and electrical appliance load levels can address the challenges posed by rapid urban growth in the Global South. This proposed method can assist municipal administrations in establishing a database that can be updated resource-efficiently at regular intervals by acquiring new satellite images.

Rapid expansion of the invasive-like red macroalga, Chondria tumulosa (Rhodophyta), on the coral reefs of the Papahānaumokuākea Marine National Monument.

A cryptogenic, invasive-like red macroalga, Chondria tumulosa, was first observed in 2016 forming thick mats on the forereef of Manawai Atoll within Papahānaumokuākea Marine National Monument. Subsequent expeditions revealed an increased abundance of this alga. In 2021, unattached C. tumulosa was observed forming a network of dark, meandering accumulations throughout the atoll's inner lagoon. High-resolution satellite imagery revealed that these accumulations became visible in 2015 (length: ~0.74 km; area: ~0.88 km2 ) and increased 56-fold in length and 115-fold in area by 2021 (length: 41.32 km; area: 101.34 km2 ). An exponential expansion rate of ~16.02 km · y-1 (length), ~44.75 km2 · y-1 (area). This study presents the comprehensive temporal and spatial expansion of C. tumulosa accumulations for Manawai Atoll since its discovery, providing ecologist and resource managers with a proxy to gauge the overall abundance trend of this invasive-like alga.

Progress toward the sustainable development of world cultural heritage sites facing land-cover changes

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of "monuments and groups of buildings" (73.2%), "sites" (19.3%), and "cultural landscapes" (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

Windthrow damage detection in Nordic forests by 3D reconstruction of very high-resolution stereo optical satellite imagery

ABSTRACT We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.

Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning

Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity.

Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries

Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries

Adaptive Dynamics of Settlement Models in the Urban Landscape of Termez (Uzbekistan) from c. 300 BCE to c. 1400 CE

The archaeological site of Ancient Termez is located in southern Uzbekistan. Despite the arid environment, the city benefited from its strategic position near two rivers, the Amu Darya and the Surkhan Darya. Its significance was mainly related to the expansion of trade routes connecting Eurasia. The city comprises several enclosures that attest long-term human-environment interactions. In order to identify the adaptive dynamics of the settlement models during an extended chronology covering the period from c. 300 BCE to c. 1220 CE (Greco-Bactrian/Yuezhi, Kushan, Kushano-Sasanian, and Islamic periods), a multidisciplinary study has been carried out, which includes: (1) archaeological excavations in several areas of the urban complex; (2) pedestrian surveying inside some enclosures and in the urban periphery; (3) an aerial survey based on high-resolution satellite imagery; (4) AMS dating of charcoal and bone samples; (5) archaeobotanical investigation through anthracological analysis; (6) zooarchaeological studies. The results point to variations in the development of the inhabited spaces, in which abandonment and occupation took place. The zooarchaeological and archaeobotanical data demonstrate the exploitation of natural resources in different environments (i.e., arid areas and irrigated land) and a certain evolution during the period considered.

Characterizing spatio-temporal patterns of coastal navigation to inform mitigation strategies for conflict between ocean users and the protected humpback whale (Megaptera novaeangliae) in Bahía de Banderas, Mexico

Characterizing specific pressures on natural resources is a key component of developing sustainable management strategies. Here we explored the spatiotemporal distribution of marine vessels in a potentially high conflict area between humpback whales and tourism activities, namely Bahía de Banderas, Mexico, from 2017 to 2023. Using high-resolution satellite imagery, we identified high-use areas and their relationship to the presence of urban areas, vessel departure points, and the coastline before, during, and after restrictions put in place during the peak of the COVID-19 pandemic. High-resolution satellite imagery (3m/px - 5m/px) retrieved through the Planet Application Program was used to perform manual detection and annotation of vessels and spatial patterns of marine navigation were characterized through point pattern analyses The spatial distribution of all marine vessel observations in the study area was not random; four high-use coastal areas closer to urban areas were identified, three of which overlap the current marine protected area established to protect whale nursing grounds. Furthermore, as the COVID-19 pandemic began, coastal navigation decreased due to lockdown before picking up again at a higher level than the pre-COVID baseline due to social distancing restrictions. While these changes were not statistically significant, they potentially indicate a behavioral change caused by COVID-19 social distancing recommendations during 2021; the amplified demand for marine vessels appears to have increased the absolute number of vessel chartering and decreased the number of passengers onboard commercial vessels. Our preliminary work provides a reproducible remote sensing methodology that can support future mitigation strategies.

Automatic labelling for semantic segmentation of VHR satellite images: Application of airborne laser scanner data and object-based image analysis

The application of deep learning methods to remote sensing data has produced good results in recent studies. A promising application area is automatic land cover classification (semantic segmentation) from very high-resolution satellite imagery. However, the deep learning methods require large, labelled training datasets that are suitable for the study area. Map data can be used as training data, but it is often insufficiently detailed for very high-resolution satellite imagery. National airborne laser scanner (lidar) datasets provide additional details and are available in many countries. Successful land cover classifications from lidar datasets have been reached, e.g., by object-based image analysis. In the present study, we investigated the feasibility of using airborne laser scanner data and object-based image analysis to automatically generate labelled training data for a deep neural network -based land cover classification of a VHR satellite image. Input data for the object-based classification included digital surface models, intensity and pulse information derived from the lidar data. The resulting land cover classification was then utilized as training data for deep learning. A state-of-the-art deep learning architecture, UnetFormer, was trained and applied to the land cover classification of a WorldView-3 stereo dataset. For the semantic segmentation, three different input data composites were produced using the red, green, blue, NIR and digital surface model bands derived from the satellite data. The quality of the generated training data and the semantic segmentation results was estimated using an independent test set of ground truth points. The results show that final satellite image classification accuracy (94–96%) close to the training data accuracy (97%) was obtained. It was also demonstrated that the resulting maps could be used for land cover change detection.

Seismic urban damage map generation based on satellite images and Gabor convolutional neural networks

Rapid assessment of urban damages after a strong earthquake is a necessary and crucial task to reduce the number of fatalities and recover socioeconomic services. In this paper, a novel deep-learning-based framework is proposed for detecting and mapping damages in urban buildings and roads using post-earthquake high-resolution satellite imagery. The method begins with overlaying a pre-event vector map on an input image to extract the building and road objects. The core machine learning components include two separate convolutional neural networks (CNN), integrated with Gabor filters, which extract debris pixels associated with building and road objects. These debris pixels are analyzed to generate the final damage maps, which show multiple damage degrees for buildings and roads. Two different datasets were used to thoroughly evaluate the proposed method's overall effectiveness. The overall accuracy of 95% for detecting the debris pixels in building and road areas proves the effectiveness of the proposed CNN models for debris detection in comparison to the traditional Machine Learning (ML) methods. The proposed method successfully labelled 84% of the buildings and 87% of the roads when compared with a manually generated multiple damage map.

HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery

Abstract. Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth observation (EO) data, several gaps and uncertainties remain with developing models to be operational at the global scale. The lack of a high-resolution globally distributed and event-diverse dataset for landslide segmentation poses a challenge in developing machine learning models that can accurately and robustly detect landslides in various regions, as the limited representation of landslide and background classes can result in poor generalization performance of the models. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD), a high-resolution (HR) satellite dataset (PlanetScope, 3 m pixel resolution) for landslide mapping composed of landslide instances from 10 different physiographical regions globally in South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall-triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions in the form of standardized image patches containing four PlanetScope image bands (red, green, blue, and NIR) and a binary mask for landslide detection. The HR-GLDD can be accessed through this link: https://doi.org/10.5281/zenodo.7189381 (Meena et al., 2022a, c). HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, three recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results when testing the HR-GLDD at individual test sites, thereby indicating the robustness of the dataset for such purposes. The HR-GLDD is open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

Enhancing the Glacial Lake Inventory in Bhutan through Semi-Automatic Techniques using Google Earth Engine and QGIS for the Phochhu Basin

Bhutan located in the Hindu Kush Himalayan (HKH) region consists of several glaciers and glacial lakes at higher elevations. With the rapid change in global temperature, glaciers are found to melt at an accelerated rate. This rapid melt of glaciers gets accumulated in weak moraine walls forming a glacial lake, posing a major threat to the downstream communities. As per the Bhutan Glacial Lake Inventory 2021, Bhutan has 567 glacial lakes. Furthermore, the Phochhu basin has the maximum glacial lakes (0.05%) and of which 9 are PDGL. Hence a need for a time monitoring system is imminent. With the availability of free High-resolution Satellite Imageries and Advanced Remote Sensing tools, it has been a sine qua no for monitoring glacial lakes in high areas. Therefore, using the Google Earth Engine and Qgis Platform a semi-automated technique was used to generate glacial lake inventories for Phochhu Sub-basin for the year 2021. We found out that there are 166 glacial lakes covering an area of 24.051 km2.

Analysis of Land Cover Changes in the Brown Canyon Mining Area Associated with Restrictions on Community Activities

The high land use as area mining has resulted in changes in land cover and impacts the surrounding environment. In 2020, the Semarang City government implemented a Restrictions on Community Activities (RCA) policy to prevent the spread of the COVID-19 virus, indirectly limiting mining activities in the area of the Brown Canyon mine. This study aims to analyze the land cover change in the Brown Canyon mine area linked to the RCA policy. Assessment of land cover change is carried out by land cover classification on multitemporal Landsat imagery using Random Forest (RF) method. Based on the classification results, it was found that there was a change in mining land cover in Brown Canyon which increased by 3.713 Ha when the RCA policy was implemented from 46.305 Ha to 50.018 Ha. Likewise, after the implementation of RCA, there was an increase of 3.510 Ha from 50.018 Ha to 53.528 Ha. The accuracy of land cover classification before RCA was 90.12%. During RCA was 88.18%, and after RCA was 89.94%. Then, if related to the results of the interview data processing, the change in mining land cover that occurred was influenced by mining activities in the study area during the RCA period. So, it can also be said that RCA does not significantly influence mining activities in the Brown Canyon mining area. Several suggestions can be used for further research, such as using high-resolution satellite imagery and having mining area boundary data for each site.

Surface Displacement of Hurd Rock Glacier from 1956 to 2019 from Historical Aerial Frames and Satellite Imagery (Livingston Island, Antarctic Peninsula)

In the second half of the 20th century, the western Antarctic Peninsula recorded the highest mean annual air temperature rise in the Antarctic. The South Shetland Islands are located about 100 km northwest of the Antarctic Peninsula. The mean annual air temperature at sea level in this Maritime Antarctic region is close to −2 °C and, therefore, very sensitive to permafrost degradation following atmospheric warming. Among geomorphological indicators of permafrost are rock glaciers found below steep slopes as a consequence of permafrost creep, but with surficial movement also generated by solifluction and shallow landslides of rock debris and finer sediments. Rock glacier surface velocity is a new essential climate variable parameter by the Global Climate Observing System, and its historical analysis allows insight into past permafrost behavior. Recovery of 1950s aerial image stereo-pairs and structure-from-motion processing, together with the analysis of QuickBird 2007 and Pleiades 2019 high-resolution satellite imagery, allowed inferring displacements of the Hurd rock glacier using compression ridge-and-furrow morphology analysis over 60 years. Displacements measured on the rock glacier surface from 1956 until 2019 were from 7.5 m to 22.5 m and surface velocity of 12 cm/year to 36 cm/year, measured on orthographic images, with combined deviation root-mean-square of 2.5 m and 2.4 m in easting and northing. The inferred surface velocity also provides a baseline reference to assess today’s displacements. The results show patterns of the Hurd rock glacier displacement velocity, which are analogous to those reported within the last decade, without being possible to assess any displacement acceleration.

Testing a novel pasture quality index using remote sensing tools in semiarid and Mediterranean grasslands

Estimating the nutritional value of herbage is a core aspect of livestock pasture management. Quantitative information related to significant findings is essential for assessing nutritional provision and preventing pasture degradation. Here, we developed a new Pasture Quality Index (PQI) that indicates the nutritional quality of pastures based on their nutritional value and the composition of plant functional groups of the vegetation. We applied the model on a regional scale using VENμS satellite data, collected more than 450 plant samples, and applied pasture quality parameters analysis. Pasture samples were collected from paddocks under two different grazing intensities in Israel in a semiarid and a Mediterranean grassland. Pasture quality was estimated five times over two years at mid- and peak vegetation growth stages. The PQI included: protein content, fiber content, dry matter digestibility and the proportion of unpalatable spiny thistles. A support vector machine regression model was used to train statistical models that relate ground truth PQI data to the reflectance values of the VENμS satellite. Large-scale forage quality maps at 5 m spatial resolution were created. Fine-resolution drone orthomosaics were used to enhance the satellite-based predictions accuracy, resulting in a model accuracy of R2 = 0.86. We observed that nutritional quality showed strong dependence on seasonality and the grazing regime, with higher quality observed during mid-growth compared to peak growth. Higher quality was also observed under grazing compared to ungrazed paddocks. The occurrence of unpalatable thistles in grazed paddocks significantly reduced the pasture quality at the Mediterranean grassland. To summarize, the PQI enables the integration of several quality indicators into an index that helps in detecting the effects of grazing management on rangelands. In addition, high accuracy was achieved in relating the overall PQI and high spatiotemporal resolution satellite imagery data. Our developed methodology enables site-specific, spatially explicit, frequent, and area-extensive pasture quality assessment that can aid in optimizing livestock management in various ecosystems.