30-m Resolution Research Articles

A twenty-year dataset of high-resolution maize distribution in China

China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces.

Soil color mapping based on Munsell system in the northeast of China

Soil color is the most readily available soil property, soil scientists often use the Munsell system to describe soil color and to quickly understand the physical, chemical, and biological properties of soil through soil color variation. Currently, soil color mapping studies often rely on indirect acquisition of Munsell color data from soil spectral information. As a result, there may be some discrepancies between the mapped colors and the colors perceived by humans. In this study, we collected 880 dry and 827 moist Munsell samples from the Northeast China to test the method developed for soil color mapping. We selected a set of covariates (climate, topography, evapotranspiration, regolith thickness, and remote sensing images) for digital soil color mapping, these covariates were related to soil formation or organic matter accumulation processes. Finally, the dry and moist topsoil color mapping with 30-m resolution was obtained for cropland in Northeast China. These covariates all played positive roles in the soil classification process, among which annual temperature mean (MAT), mean annual precipitation (MAP), land surface temperature (LST), and mean annual potential evapotranspiration (MAPET) had the highest contribution. We summarized the difficulties that usually occur in soil color mapping, and put forward a ‘stepwise classification’ method. According to the three-dimensional attributes of the Munsell system, the measured color data were divided into seven color groups (four dry and three moist color groups). Among all color groups, the highest accuracy of mapping validation was 0.69 and the lowest was 0.58. The kappa coefficient, usually affected by classification bias, was small (0.3). The color differences between measured and predicted colors were found to be 1.21 (dry color) and 1.36 (moist color). Compared to direct classification, the stepwise classification method improved classification accuracy and reduced mapping color differences. Our soil color mapping for two cases of dry and moist corresponded well with spectral color maps. The correlation between soil organic matter (SOM) content and soil color (dry and moist) was analyzed in 807 samples. The darker the soil color was, the higher the SOM content was. In summary, soil color holds the potential to serve as a surrogate for assessing cropland quality, particularly in evaluating SOM.

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-m resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and improve the generalization capability of the model. We tested the model on hold out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We noticed NIR band at the early and late stage of the apple harvest season (main crop in the region) to be of critical importance for the model. Finally, we used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps, and made the code open-source.

Conflicts among ecosystem services may depend on environmental awareness: a multi-municipality analysis

Abstract Timber and non-timber ecosystem services (ESs) of forests can have trade-offs. These trade-offs are often influenced by local characteristics, and a higher awareness of local ESs among the location population could support forest management to supply ESs sustainably. This study examines trade-offs among timber and non-timber ESs in three adjacent municipalities in Japan where social contexts differ and discusses them in relation to the environmental awareness of each community. First, we explored the local awareness of the population of ESs in interviews. Then we produced maps of landslide prevention, sediment retention, and forest recreation ESs in plantations at 30-m resolution and classified forests according to evaluations of each ES. We overlaid the ES maps with a map of logging locations from the previous 5 years to calculate the logged ratio for each ES class. In a region with a long history of forestry, where awareness of ESs seems to be high, forests providing a wider range of ESs had a lower logged ratio than forests with lower ESs. In contrast, in a region in which contracted foresters from outside of that region were coordinating logging activities, even forests providing numerous ES had a high logged ratio. Thus, increasing awareness of ESs amongst the local population may lead to a more balanced use of ESs. Our results indicate that local governments would be best placed to raise awareness by educating forest enterprises or providing science-based information on ESs to foresters. We conclude that analyses of local ES trade-offs under consideration of the social context as presented here, is the first step towards developing and maintaining sustainable forest management principles.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016–2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies.

Assessment of influencing level of rainfall and physical factors on groundwater level for a semi-arid flat terrain watershed using grid-based geospatial analysis: a case study from Lower Palar Basin, Tamil Nadu, India.

Understanding natural phenomena with the help of modern scientific approaches helps to reach sustainable solutions for current and future water-related problems. In this context, present study aims to assess relative influencing level of physical factors in controlling groundwater level, using a novel grid-based delineation technique, in Lower Palar River Basin, in Kanchipuram and Chengalpattu districts of South India. The influencing factors viz-a-viz: rainfall, soil texture, land use/land cover, terrain slope, geomorphology, lithology, and drainage characteristics were considered for the study. Archived data (2011 to 2020) of monthly rainfall at four rain gauge stations and monthly groundwater level of 22 locations, soil texture, lithology, and geomorphology data were considered for the study. SRTM digital elevation model with 30-m resolution was used for analyzing drainage characteristics and terrain slope. Thematic maps for considered factors were prepared, using common grid delineation method in GIS platform that divided study area into 52 grids, to inter-relate the discrete and continuous parameters with groundwater level. Results indicate that level of influence increases in the order of precipitation followed by lithology, land use/land cover, terrain slope, geomorphology, infiltration number, and soil texture. The study shows groundwater resilience is highly influenced by soil texture and infiltration number compared to other factors considered. It can be concluded that grid-based delineation successfully identifies grids with significant influence of individual factors by comparing with groundwater resilience. Common grid-based delineation method proves to be more effective in assessing groundwater resilience and can be used more efficiently in groundwater studies.

Wetland mapping in East Asia by two-stage object-based Random Forest and hierarchical decision tree algorithms on Sentinel-1/2 images

Accurate information on wetland extent in East Asia is essential to assess progress towards Sustainable Development Goals (SDGs) and the use of wetland resources, where wetlands benefit a quarter of the world's population and millions of wild birds in over three global migratory corridors. In this study, using 122,128 Sentinel-1 and 89,752 Sentinel-2 images acquired in 2021 available on the Google Earth Engine platform, we developed a novel two-stage classification for continental-scale wetland mapping and generated the first and up-to-date 10-m resolution wetland map of East Asia. Such a two-stage classification method, which integrates automatic sample generation and spatiotemporal features, combined an initial object-based random forest classifier with a subsequent hierarchical decision tree for secondary waterbody types. The resulting comprehensive map with 3 broad categories and 12 sub-categories in East Asia, named EA_Wetlands, achieved over 88% overall accuracy. According to EA_Wetlands, the total wetland area in this region is 481,802.49 km2, mainly distributed in Northeast China and the Qinghai-Tibet Plateau (41.02%). Of all wetlands in East Asia, about 68.26% are inland wetlands. The highest proportion (29.67%) is identified for inland marsh among 12 sub-categories. Among five countries, China has >88% of the total wetland resources in East Asia, followed by Mongolia (3.57%). South Korea has the largest ratio between wetland and national land areas (10.43%). EA_Wetlands, as the first 10-m resolution wetland data product, will have great applications and benefit wetland conservation and policy management. It will be a critical support for evaluating the implementation of wetland-related international conventions at country and continental scale in East Asia.

Downscaling of vegetation indices from multi-satellite throughout-season maize

Phenomenology of the growing season The Normalized Difference Vegetative Index (NDVI) provided by satellites was employed as a replacement for quantifying the output of vegetative biomass. The MODIS sensors 250-m data have been utilized for terrestrial ecosystem modelling and monitoring. MODIS’s land surface data are credible and trustworthy because to their high temporal resolution and broad spectrum of wavelengths. Land cover and land change studies have used the spatially accurate data provided by the Landsat 30m to characterize human-scale processes. Sentinel-2 is a land surveillance satellite with innovative spectrum capabilities, extensive coverage, and excellent spatial and temporal resolutions. The primary purpose of this work is to create a downscaling vegetation indices (VI) database by combining MODIS, Landsat, and Sentinel data into 250m resolution. The most important NDVI indicates the maize growing season in April and August. MODIS, Landsat, and Sentinel 250m derived biophysical information deliver the same biophysical information for moderate-scale biological aspects. This multi-sensor inquiry also includes high-resolution Landsat data, which will be useful for local ecological investigations while keeping the full seasonal dynamic information given by MODIS.

High-Precision Single-Leak Detection and Localization in Single-Phase Liquid Pipelines Using the Negative Pressure Wave Technique: An Application in a Real-Field Case Study

Summary This paper presents a novel workflow for high-precision leak detection in pipeline networks using the negative pressure wave (NPW) technique. The proposed workflow addresses challenges associated with noisy and convoluted pressure transducer data, rapid pressure decay, and the need for robustness in leak event detection. To overcome these challenges, the workflow incorporates data preprocessing techniques for cleansing, normalization, and denoising, as well as dynamic pressure control limit lines to differentiate between pump and leak events. Multiple transducer analysis techniques are used to minimize false positives. Synthetic leak scenarios are generated using the Water Network Tool for Resilience (WNTR) package, enabling a comprehensive assessment of the workflow’s performance. The generated scenarios are validated through pressure history matching against field inline pressure recordings. A dashboard is developed for real-time visualization and verification of leak events. The effectiveness of the workflow is demonstrated through testing on a real network, resulting in the successful detection and precise localization of a confirmed leak event. The workflow proves its capability to achieve high accuracy, with a 100-m resolution in a complex network configuration with 29 pipe sections and 1-Hz pressure signal recordings. For synthetic leak events, a 10-Hz pressure signal is utilized, achieving a remarkable 10-m accuracy. Moreover, the integration of the workflow with supervisory control and data acquisition (SCADA) systems is showcased, highlighting its potential for near real-time leak detection in practical applications. Overall, this paper presents a comprehensive and effective workflow for high-precision leak detection and localization in pipeline networks, offering valuable insights into improving the efficiency and reliability of leak detection systems.

Understanding the impact of heatwave on urban heat in greater Sydney: Temporal surface energy budget change with land types

The impact of heatwaves (HWs) on urban heat island (UHI) is a contentious topic with contradictory research findings. A comprehensive understanding of the response of urban and rural areas to HWs, considering the underlying cause of surface energy budget changes, remains elusive. This study attempts to address this gap by investigating a 2020 HW event in the Greater Sydney Area using the Advanced Weather Research and Forecasting (WRF) model with 250-m high resolution. Findings indicate that the HW intensifies the nighttime surface UHI by approximately 4 °C. An analysis of surface energy budgets reveals that urban areas store more heat during the HW due to receiving more solar radiation and less evapotranspiration compared to rural areas. The maximum heat storage flux in urban during the HW can be around 200 W/m2 higher than that during post-HW. The stored heat is released at nightime, raising the air temperature in the urban areas. Forests and savannas have relatively lower storage heat fluxes due to high transpiration and albedo, and the maximum heat storage flux is only around 50 W/m2 higher than that during post-HW. In contrast, a negative synergistic effect is detected between the 2-m UHI and HW. This may be because other meteorological conditions including wind have substantial impacts on the air temperature pattern. The strong hot and dry winds coming from the west resulted in a higher air temperature in the western urban district, and intra-city disparities are higher. Meanwhile, the western forest area also experiences higher temperatures due to the westward winds. In addition, changes in wind direction alter the temperature distribution in the northern rural region. The findings of the present study may provide some insights into urban heat mitigation during HW.

SDGSAT-1 nighttime light data improve village-scale built-up delineation

While previous urban delineation studies have been based mainly on Defense Meteorological Satellite Program (DMSP) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) data at a 1-km or 500-m spatial resolution, the higher 10-m-spatial-resolution Sustainable Development Science Satellite-1 (SDGSAT-1) NTL data offer the possibility of sophisticated village-scale analysis. In this study, we developed the quantile method with downward heuristic rules for built-up delineation based on SDGSAT-1 NTL data and proposed a new threshold method, named the trend method framework, for urban dynamic delineation in combination with SDGSAT-1 NTL data and DMSP-VIIRS NTL data. Then, the methods developed in this study were applied to five urban agglomerations (the Beijing-Tianjin-Hebei Urban Agglomeration (BTH), Yangtze River Delta (YRD), Guangdong–Hong Kong–Macao Greater Bay Area (GHM), Chengdu–Chongqing Economic Circle (CCC), and Hainan Free Trade Port (HTP)) in China. The results showed that both the downward quantile method and the trend method can be used to delineate village-scale built-up areas. In the delineation of built-up areas based on SDGSAT-1 NTL data, the trend method provides clearer information on the built-up areas within lighted areas, leading to higher accuracy than the downward quantile method. In the built-up area delineation based on DMSP and VIIRS NTL data at 1-km resolution, the trend method combines SDGSAT-1 NTL data and accounts for information on time-series changes in urbanization, thus allowing for the more accurate extraction of built-up areas. In 2020, the trend method had higher delineation accuracies than the quantile method, with effectiveness accuracies (EAs) of 0.91, 0.77, 0.67, 0.94, and 0.65 for the YRD, BTH, CCC, GHM, and HTP, respectively, compared with the European Space Agency (ESA) land use and land cover (LULC) product. The trend method has very good application prospects for delineating built-up areas using the high-resolution SDGSAT-1 NTL dataset.

Comparing the Effectiveness of Different Machine Learning Algorithms for Crop Cover Classification Using Sentinel 2

Crop cover mapping is an essential tool for controlling and enhancing agricultural productivity. By determining the spatial distribution of different crop types, solidified judgements regarding crop planning, crop management, and risk management can be made. Crop cover classification using optical data pose constraints in terms of spatial and spectral resolution. With Sentinel – 2 data providing the ground information at 10m resolution, users may choose the best spectral band combinations and temporal frame by analysing the spectral-temporal information of different crops. The crop categorization map for the Kallakurichi and Villupuram districts were created in this study using the Random Forest (RF) and Decision tree (C5.0) classifiers. The study mainly focuses on comparing the classification accuracy of two classifiers and figuring out the best classifiers for crop cover mapping with respect to the study area. The ground truth information collected, were partitioned into calibration and validation datasets and the validation resulted with the Overall Accuracy (OA) and kappa coefficient of 66%; 0.63 and 60%; 0.67 for RF and C5.0 algorithms, respectively. From the results, it could be concluded that the RF classifier performed comparatively better than C5.0, thus making it suitable for crop cover classification.

A Study of Land Use and Gully Erosion in Nekede Community, Owerri West, Imo State, Nigeria Using the GIS

Land use and gully erosion incidence in Nekede community, Owerri Imo state was studied using some digital tools of the GIS and remote sensing. The Nigeria sat. 2 imagery sourced from the National Space Research and Development Agency (NASRDA) was acquired from the Landsat ETM sensor with a resolution of 30m. Landsat TM and ETM data obtained had cloud cover of less than 20%. The images were geo-referenced to a Universal Transverse Mercator (UTM) grid using the software to allow compatibility and comparison with other data sets. The Shuttle Radar Topography Mission (SRTM) 30m DEM of the study area was downloaded from the CIAT-CSI SRTM website. These data were projected to the UTM coordinates system and clipped to the extent of the study area. The Normalized Difference Vegetation Index (NDVI) was equally used to show some details since a ratio of red and near infra-red bands from a remotely-sensed image-greenness can be defined. Results are that the colour differences found in a large part of the south-west indicated built-up but were much earlier in time covered by dense forest cover which the inhabitants cleared in the process of urbanization and agricultural activities exposing the fragile soil to heavy rainfall and concentrated run off. The high speed of the surface runoff leads to rapid washing away of the soil and weakening the soil strata bringing in effect gullies. Land use zoning using digital GIS and remote sensing models in monitoring land use/cover changes following existing master plans was among others recommended.

Soil moisture at 30 m from multiple satellite datasets fused by random forest

As a key variable used to characterize the climate process between the land surface and atmosphere, the surface soil moisture (SM) plays an irreplaceable role in the fields of hydrology, meteorology and agriculture. However, the relatively coarse spatial resolution of SM products currently limit the application in water resource management at the field scale. In this study, we proposed a high-spatial-resolution SM retrieval framework based on the random forest algorithm (RF-SM) to integrate in situ SM dataset from in the International Soil Moisture Network (ISMN), Landsat 8 optical and thermal observations, soil properties from SoilGrids V2.0, meteorological variables from the fifth generation of the European ReAnalysis (ERA5) dataset and four coarse-scale SM products including the Soil Moisture Active/Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), European Space Agency Climate Change Initiative (ESA CCI) and U.S. National Climate Assessment Land Data Assimilation System (NCA-LDAS). Compared to three other machine learning (ML) algorithms [extremely randomized trees (Extra-Trees), gradient boosting regression tree (GBRT) and extreme gradient boosting (XGBoost)], the random forest (RF) algorithm exhibited the best performance against a subset of 100 validation sites with a Kling–Gupta efficiency (KGE) of 0.69 and root-mean-square-error (RMSE) of 0.063 m3/m3. In terms of different land cover types and typical sites, RF-SM also showed a better accuracy than any of the individual SM product. Finally, the retrieval framework was applied to map the 30-m resolution SM spatial distributions in five substudy areas in the U.S. The results suggest that it is feasible to retrieve accurate SM at a 30-m spatial resolution from multiple satellite datasets based on the RF algorithm, which has important practical significance for agricultural drought monitoring at the field scale.

Two-stage scheduling of smart electric vehicle charging stations and inverter-based Volt-VAR control using a prediction error-integrated deep reinforcement learning method

Smart electric vehicle charging stations (EVCSs) having distributed energy resources (DERs), including photovoltaic (PV) systems and energy storage systems (ESSs), are becoming vital devices for increasing their profit and maintaining stable distribution grid operations by scheduling the real/reactive power of DERs. However, prediction errors of PV generation outputs and electric vehicle (EV) loads from EVCSs may decrease their profit and destabilize the distribution grid owing to incorrect EV charging scheduling and voltage regulation. To address this issue, we propose a two-stage framework for smart EVCS scheduling and inverter-based Volt-VAR control (VVC) using prediction error-integrated deep reinforcement learning (DRL). In the first stage, the EVCS agents train their neural network model with a 30-min resolution to maximize their profit through a day-ahead charging/discharging scheduling of ESSs in the EVCSs while responding to various prediction errors of PV generation outputs and EV loads. The total real power consumption of each EVCS, including the charging/discharging schedules of the ESSs calculated in the first stage, is delivered to the second stage, in which the VVC agent trains its neural network model with a 5-min resolution to minimize the real power loss and voltage violations through real-time reactive power scheduling of the ESSs in the EVCSs via their inverters. The proposed approach was tested in the IEEE 33-node and IEEE 123-node distribution systems. The results show that the proposed approach outperforms DRL methods that do not consider the prediction errors in terms of profitability of the EVCS and reduction of real power loss.

Hierarchical extraction of cropland boundaries using Sentinel-2 time-series data in fragmented agricultural landscapes

Accurately extracting cropland field parcels from satellite data in fragmented agricultural landscapes presents huge challenges due to small field sizes and irregular field shapes. Here, we developed a hierarchical framework for agricultural field boundary extraction from Sentinel-2 satellites based on the concept of the degree in field fragmentation. Our framework focused on three tasks, including a core task for agricultural field extraction and two auxiliary tasks to address special situations in two different fragmented regions, namely the Plains-Basin scenes with relatively regular shapes and diverse crop types, and the Plateau-Hilly scenes, with irregular shapes and small field sizes. First, the field boundaries were delineated using a modified Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net), and post-processed using morphological filtering and the Douglas-Peucker algorithm (DPA). A rule-based decision fusion strategy assisted with a classification method was developed to address the under-segmentation problem in the Plains-Basin scenes, and an adaptive generalized buffer algorithm (AGBA) was used to address the incomplete-segmentation problem in the Plateau-Hilly scenes. We tested the proposed methods in Yangming County, Heilongjiang Province of China, using time series Sentinel-2 imageries with 10-m spatial resolution. Compared with the results from using R2U-Net or a single-temporal image alone, the fragmented field boundaries show an outstanding performance, with an overall accuracy of 86.42% and 84.15% and F1 score of 0.85 and 0.83 in Plains-Basin scenes and Plateau-Hilly scenes, respectively. Our proposed methods provide a feasible solution to finely extract field boundaries in highly fragmented and heterogeneous agricultural landscapes.

Downscaling of Thermal Images Over the Gaza Strip Using the Land Surface Temperature—Spectral Indices Relation: Case Study; Hot, Arid, and Semi-Arid Areas

Many thermal sharpening applications are evaluated in temperate and subtropical climate regions, which are com-monly characterized by the presence of the urban heat island (UHI) phenomenon. However, similar studies are rarely found in hot, arid, and semi-arid climate cities, where an urban cool island (UCI) phenomenon exists. Recent research shows that the spectral characteristics of land covers and their responses to LST are different based on their climatic type. Resultantly, spectral indices (SIs) show different evaluations to be successfully used in sharpen-ing techniques like the DisTrad, to sharpen LST over several land covers, especially in urban areas. The main ob-jectives of this study are; 1)- to evaluate the spatial relationship between LST and a number of most commonly used urban's remote sensing SIs (21 SIs described in table 2) over the Gaza Strip in two land cover scenarios: all land covers "All" and the urban mask “urban”; 2)- to downscale aggregated low-resolution Landsat 8\LST image at 1000 m to a higher resolution of 100 m. Spectral indices and land surface temperature are calculated using the Landsat 8 image of summer 2017. Spatial regression analysis between SIs and LST within the "All" land cover class and at 1000 m resolutions show the best SIs that have the highest correlation (R2) with LST are DBSI (0.66) and ABEI (0.59). While in the "urban" class, the same indices shows also the highest correlation; BAEI (0.57) and DBSI (0.64). Moreover, statistical validation with LST observation at 100m resolution (Landsat 8\LST), DisTrad was found suc-cessful to downscale LST to 100 m resolution over UCI areas using the indices ABEI and DBSI with the highest cor-relation (R2) over the "All" class (0.77 and 0.73 respectively) and over the "urban" mask using DBSI (0.59) and BAEI (0.58)

Scalable early detection of grapevine virus infection with airborne imaging spectroscopy.

The US wine and grape industry suffers $3B in damages and losses annually due to viral diseases such as Grapevine Leafroll-associated Virus Complex 3 (GLRaV-3). Current detection methods are labor intensive and expensive. GLRaV-3 undergoes a latent period in which the vines are infected but do not yet display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. We deployed the NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after imagery acquisition. In both Sept. 2020 and 2021, industry collaborators scouted 317ac on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Grapevines identified as visibly diseased in 2021, but not 2020, were assumed to have been latently infected at time of acquisition. We trained spectral models with random forest and synthetic minority oversampling technique to distinguish non-infected and GLRaV-3-infected grapevines. Non-infected and GLRaV-3 infected vines could be differentiated both pre- and post-symptomatically at 1m through 5m resolution. The best-performing models had 87% accuracy distinguishing between non-infected and asymptomatic vines, and 85% accuracy distinguishing between non-infected and asymptomatic + symptomatic vines. The importance of non-visible wavelengths suggests this capacity is driven by disease-induced changes to overall plant physiology. Our work sets a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring.

Ship detection using Google Earth Pro and SAR Sentinel-1 Satellite in Karimunjawa, Indonesia

Karimunjawa is one of the tourist icon due to the beauty of its coral reefs. Many tourists visit for snorkeling and diving in Karimunjawa which requires ships for transportation. This study aims to determine the distribution of ships in Karimunjawa before and during the COVID-19 pandemic using the Google Earth Pro satellite and Synthetic Aperture Radar (SAR) Sentinel-1. There were 6 islands selected to represent the anchorage level based on the number of tourist arrivals. The results of this study indicate that the Sentinel-1 SAR has high capability in detecting ships by using the Constant False Alarm Rate (CFAR) algorithm method, which was detected with a total of 83 ships on 31st March 2021 in six island representatives. This funding is supported by SAR images that have a high spatial resolution of 5m, moreover, ships are typically constructed from large flat metal sheets and hence are usually radar bright and detectable in SAR imagery. Even though Google Earth Pro has a higher resolution of 30 cm, the ship detection process is identified manually. This study showed the Covid-19 pandemic condition had a significant decreasing effect on the number of ships detected, there was a 41.6% decrease from 2019 to 2020 and a 41.95% decrease from 2020 to 2021 due to the temporary closure of Karimunjawa.

Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.