Spatio-temporal Variability of Lightning Climatology and Its Association With Thunderstorm Indices Over India

Lightning is an electrical discharge - a'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in three days’ span due to lightning events. In this work, Lightning Imaging Sensor (LIS) information from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1 X 0.1 degree has been utilized to create the climatology of India for 16 years from 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite low-resolution monthly time series (LRMTS) with 2.5-degree resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening (1400-1900 Hrs) time duration around 0.001 flashes/km2/hr. The highest lightning occurred in May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon (MAM) ranges from 0.248 – 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 – 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flashes mainly at North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu & Kashmir region. The CAPE and K Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated. This study also focused on finding of lightning hotspots region of India district wise and Rajouri district in Jammu and Kashmir got the highest lightning with 121 flashes/km2/yr.

Avaliação da acurácia posicional de vértices obtidos por imagem de sensor orbital e aerofotogrametria para fins de georreferenciamento de imóveis rurais

Em sua grande maioria, o georreferenciamento de imóveis rurais tem sido realizado somente com o levantamento geodésico (LG) por meio de receptores GNSS. Porém, é possível realizá-lo por meio de imagens de satélites e imagens aerotransportadas. A utilização de imagens orbitais ou aerotransportadas pode reduzir o tempo de serviço e auxiliar em limites inacessíveis e naturais. O maior problema em realizar o georreferenciamento utilizando imagens está em atender às precisões exigidas pelo Instituto Nacional de Colonização e Reforma Agrária (INCRA), em razão do imageamento ser menos preciso que o levantamento geodésico. Outra dificuldade está em identificar feições que se encontram sob matas. Entretanto, no mercado existem imagens de satélite de alta resolução espacial e também existe a possibilidade de obtenção de imagens coletadas por aeronaves remotamente pilotadas (ARP) com altíssima resolução espacial que podem atender as exigências. Deste modo este trabalho tem como objetivo avaliar as feições obtidas por três imagens, uma WorldView-3, uma PlanetScope e por uma ortofoto de ARP, sendo estas três comparadas e avaliadas a partir do LG por meio de receptores GNSS. Entre os conjuntos de dados utilizados o melhor resultado de acordo com a classificação normativa do INCRA foi a ortofoto gerada pelo levantamento aerofotogramétrico, pois atendeu à precisão para os vértices artificiais, naturais e vértices inacessíveis. No entanto, a imagem WorldView-3 apresentou o pior resultado na classificação, pois não atendeu nenhum dos tipos de vértices. Entre os três conjuntos de dados utilizados recomenda-se utilizar o levantamento aerofotogramétrico para realizar o georreferenciamento de imóveis rurais. Evaluation of the positional accuracy of features obtained by images of orbital sensors and airborne for georeferencing of rural propertiesA B S T R A C TConcerning methods of positioning the georeferencing of rural properties, it stands out the topographical and geodetic surveys. However, it is possible to make through remote sensing (images of orbital sensors and airborne). The use of orbital or air-bone images can reduce service time and help in inaccessible areas, such as unreachable and natural limits. The most significant difficulty of the georeferencing using images is to meet the required accuracy by the National Institute of Colonization and Agrarian Reform (INCRA). However, there are high spatial resolution satellite images are now available. There is the possibility of getting the images collected by remotely piloted aircraft (RPA) with a very high spatial resolution that meets the requirements. This work aims to assess the features obtained by three images, a WorldView-3, a Planet Scope, and an RPA orthophoto. These three are being compared and evaluated from a geodetic survey and subsequently classified according to the cartographic precision standard of INCRA. The best dataset for the normative of INCRA was the orthophoto generated by RPA because it met the precision for artificial, natural vertices and inaccessible vertices. However, the WV-3 image had the worst result in the classification because it did not meet consistent accuracy to any of the vertices' types. Between the three data sets used, the one that best suits the specifications of georeferencing of rural properties were the images airborne.Key words: Remote Sensing, INCRA Rules, Aerophotogrammetric Survey, Cartographic Accuracy Standard.

Realization of a polarization-insensitive optical frequency-domain reflectometer using an I/Q homodyne detection

We report on the development and implementation of an optical frequency-domain reflectometer (OFDR) sensing platform. OFDR allows to measure changes in strain and temperature using optical fibers with a length of several tens of meters with very high spatial resolution. We discuss the operation principles and challenges to implement an OFDR system using optical homodyne detection based on a dual-polarization 90° optical hybrid. Our setup exhibits polarization and phase diversity, fully automated data acquisition and data processing using a LabVIEW-based implemented software environment. Using an optical hybrid enables to discriminate phase, amplitude and polarization by interfering the Rayleigh scatter signal and a local oscillator with four 90° phase stepped interferences between the two signals. Without averaging and a fast acquisition time of 230 ms, our preliminary results show a spatial resolution of 5 cm and a temperature resolution of about 0.1 Kelvin on a 3 m-long fiber.

Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions

With an increasing global population, accurate and timely population counts are essential for urban planning and disaster management. Previous research using contextual features, using mainly very-high-spatial-resolution imagery (<2 m spatial resolution) at subnational to city scales, has found strong correlations with population and poverty. Contextual features can be defined as the statistical quantification of edge patterns, pixel groups, gaps, textures, and the raw spectral signatures calculated over groups of pixels or neighborhoods. While they correlated with population and poverty, which components of the human-modified landscape were captured by the contextual features have not been investigated. Additionally, previous research has focused on more costly, less frequently acquired very-high-spatial-resolution imagery. Therefore, contextual features from both very-high-spatial-resolution imagery and lower-spatial-resolution Sentinel-2 (10 m pixels) imagery in Sri Lanka, Belize, and Accra, Ghana were calculated, and those outputs were correlated with OpenStreetMap building and road metrics. These relationships were compared to determine what components of the human-modified landscape the features capture, and how spatial resolution and location impact the predictive power of these relationships. The results suggest that contextual features can map urban attributes well, with out-of-sample R2 values up to 93%. Moreover, the degradation of spatial resolution did not significantly reduce the results, and for some urban attributes, the results actually improved. Based on these results, the ability of the lower resolution Sentinel-2 data to predict the population density of the smallest census units available was then assessed. The findings indicate that Sentinel-2 contextual features explained up to 84% of the out-of-sample variation for population density.

UAV reveals substantial but heterogeneous effects of herbivores on Arctic vegetation

Understanding how herbivores shape plant biomass and distribution is a core challenge in ecology. Yet, the lack of suitable remote sensing technology limits our knowledge of temporal and spatial impacts of mammal herbivores in the Earth system. The regular interannual density fluctuations of voles and lemmings are exceptional with their large reduction of plant biomass in Arctic landscapes during peak years (12–24%) as previously shown at large spatial scales using satellites. This provides evidence that herbivores are important drivers of observed global changes in vegetation productivity. Here, we use a novel approach with repeated unmanned aerial vehicle (UAV) flights, to map vegetation impact by rodents, indicating that many important aspects of vegetation dynamics otherwise hidden by the coarse resolution of satellite images, including plant–herbivore interactions, can be revealed using UAVs. We quantify areas impacted by rodents at four complex Arctic landscapes with very high spatial resolution UAV imagery to get a new perspective on how herbivores shape Arctic ecosystems. The area impacted by voles and lemmings is indeed substantial, larger at higher altitude tundra environments, varies between habitats depending on local snow cover and plant community composition, and is heterogeneous even within habitats at submeter scales. Coupling this with spectral reflectance of vegetation (NDVI), we can show that the impact on central ecosystem properties like GPP and biomass is stronger than currently accounted for in Arctic ecosystems. As an emerging technology, UAVs will allow us to better disentangle important information on how herbivores maintain spatial heterogeneity, function and diversity in natural ecosystems.

Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X).

Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite

The detection of methane emissions from industrial activities has been identified as an effective climate change mitigation strategy. These industrial emissions, such as from oil and gas (O&amp;G) extraction and coal mining, typically occur as large plumes of highly concentrated gas. Different satellite missions have recently shown potential to map such methane plumes from space. In this work, we report on the great potential of the WorldView-3 (WV-3) satellite mission for methane mapping. This relies on its unique very high spatial resolution (up to 3.7 m) data in the shortwave infrared part of the spectrum, which is complemented by a good spectral sampling of the methane absorption feature at 2300 nm and a high signal to noise ratio. The proposed retrieval methodology is based on the calculation of methane concentration enhancements from pixel-wise estimates of methane transmittance at WV-3 SWIR band 7 (2235–2285 nm), which is positioned at a highly-sensitive methane absorption region. A sensitivity analysis based on end-to-end simulations has helped to understand retrieval errors and detection limits. The results have shown the good performance of WV-3 for methane mapping, especially over bright and homogeneous areas. The potential of WV-3 for methane mapping has been further tested with real data, which has led to the detection of 26 independent point emissions over different methane hotspot regions such as the O&amp;G extraction fields in Algeria and Turkmenistan, and the Shanxi coal mining region in China. In particular, the detection of very small leaks (< 100 kg/h) from oil pipelines in Turkmenistan shows the game-changing potential of WV-3 to map industrial methane emissions from space.

Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height>2 meters. Here, we report a new forest cover data product that used the FAO forest definition (tree cover>10% and tree height>5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

Modeling spatiotemporal Aedes aegypti risk in French Guiana using meteorological and remote sensing data

Although the development of vaccines for the prevention of arboviral diseases has been a priority in recent years, prevention strategies continue to depend on vector control. Risk maps at scales appropriate for these strategies can provide valuable information to assess entomological risk levels and guide actions. We used a spatio-temporal modeling approach to predict, at the local scale, the risk of homes potentially harboring Aedes aegypti larvae. The model used integrated larvae risk data collected in the field from September 2011 to February 2013, environmental data obtained from very high spatial resolution Pleiades imagery, and daily meteorological data, collected in the city of Matoury in French Guiana. Various environmental and meteorological conditions were identified as risk or protective factors for the presence of immature stages of Aedes aegypti in homes on a given date and used to produce dynamic maps with high spatial and temporal resolution. Aedes vector risk was modeled between 50 and 200 m, around houses, on a time scale of 3 to 5 days. The resulting model was extrapolated to other municipalities with the same characteristics of urbanization during the 2019-2020 dengue epidemic in French Guiana. This work represents a major opportunity to monitor the evolution of vector risk and constitutes information that could be particularly useful for public health authorities in charge of vector control.