Base Of Remote Sensing Data Research Articles

Contrasting nature of aerosols over South Asian cities and its surrounding environment

Cross-country assessment of aerosol loading was made over several South Asian megacities using multiple high-resolution remote-sensing database to assess how aerosols vary within the city and its suburbs. Parameters sensitive to aerosol optical and microphysical properties were processed over city-core and its surrounding, separated by a buffer. Cities across the Indo-Gangetic Plain (IGP; AOD:0.52–0.72) along with Mumbai (0.47) and Bangalore (0.46) denote comparatively high aerosol loading against non-IGP cities. City-core specific AOD was invariably high compared to surrounding, however with varying gradient having robust geographical signature. Exceptions to this general trend were in Kathmandu (ΔAOD: 0.07) and Dhaka (ΔAOD: 0.01) while strong positive AOD gradient was noted in Bangalore (+0.11), Colombo (+0.08) and in Mumbai (+0.07). While all mainland cities exhibited robust intraannual variability, distinction between city-core and its surrounding AOD exhibited varying seasonality. City-specific geometric coefficient of variation indicated insignificant association with mean AOD as opposed to European and American cities. Both pixel-based and city-specific analysis revealed a strong increasing trend in AOD with highest magnitude in Varanasi and Bangalore. Aerosol sub-types based on aerosols’ sensitivity to UV-absorption and particle size denotes higher relative abundance of carbonaceous smoke aerosols within city-core, without having significant distinction for mineral dusts and urban aerosols.

Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization

Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote-sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities in handling unseen categories and strong domain adaptation capabilities in handling unseen novel remote-sensing data. The method’s scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote-sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online.

Use of Deep Learning and Cloud Services for Mapping Agricultural Fields on the Example on the Base of Remote Sensing Data of the Earth

Use of Deep Learning and Cloud Services for Mapping Agricultural Fields on the Example on the Base of Remote Sensing Data of the Earth

Calibration of groundwater seepage against the spatial distribution of the stream network to assess catchment-scale hydraulic properties

Abstract. The assessment of effective hydraulic properties at the catchment scale, i.e., hydraulic conductivity (K) and transmissivity (T), is particularly challenging due to the sparse availability of hydrological monitoring systems through stream gauges and boreholes. To overcome this challenge, we propose a calibration methodology which only considers information from a digital elevation model (DEM) and the spatial distribution of the stream network. The methodology is built on the assumption that the groundwater system is the main driver controlling the stream density and extension, where the perennial stream network reflects the intersection of the groundwater table with the topography. Indeed, the groundwater seepage at the surface is primarily controlled by the topography, the aquifer thickness and the dimensionless parameter K/R, where R is the average recharge rate. Here, we use a process-based and parsimonious 3D groundwater flow model to calibrate K/R by minimizing the relative distances between the observed and the simulated stream network generated from groundwater seepage zones. By deploying the methodology in 24 selected headwater catchments located in northwestern France, we demonstrate that the method successfully predicts the stream network extent for 80 % of the cases. Results show a high sensitivity of K/R to the extension of the low-order streams and limited impacts of the DEM resolution as long the DEM remains consistent with the stream network observations. By assuming an average recharge rate, we found that effective K values vary between 1.0×10-5 and 1.1×10-4 m s−1, in agreement with local estimates derived from hydraulic tests and independent calibrated groundwater model. With the emergence of global remote-sensing databases compiling information on high-resolution DEM and stream networks, this approach provides new opportunities to assess hydraulic properties of unconfined aquifers in ungauged basins.

An active learning method with entropy weighting subspace clustering for remote sensing image retrieval

The increasing volume of high-resolution satellite imagery made image retrieval a demanding research field in the remote sensing (RS) community. The RS data are often complex, with varied temporal and spatio-spectral properties, and are unlabeled, and the process of labeling is relatively expensive. Therefore, developing sophisticated models for retrieving relevant images from RS databases is necessary, especially when few labeled samples are available. Motivated by this fact, we propose a new cluster-guided active learning (CG-AL) framework for remote sensing image retrieval (RSIR). The CG-AL-RSIR utilizes an entropy weighting K-Means subspace (EWKMS) clustering algorithm for influencing an active learning (AL) framework using a support vector machine (SVM). The AL framework efficiently selects the most informative samples for labeling depending on a weighted entropy uncertainty sampling (WtEUS). A bag-of-visual-words representation of the scale-invariant feature transform (SIFT) image descriptors is extracted for the retrieval. The EWKMS clustering addresses the high dimensionality of the RSI dataset efficiently in obtaining quality clusters for labeling The CG-AL-RSIR resulted in an overall accuracy of up to 94.14% and 92.48% for the UCM and SIRI-WHU datasets, respectively, and an ANMRR value of 0.290 for UCM and 0.326 for SIRI-WHU datasets, utilizing less number of labeled samples.

A unique bacterial and archaeal diversity make mangrove a green production system compared to rice in wetland ecology: A metagenomic approach

Mangrove provides significant ecosystem services, however, 40% of tropical mangrove was lost in last century due to climate change induced sea-level rise and anthropogenic activities. Sundarban-India, the largest contiguous mangrove of the world lost 10.5% of its green during 1930–2013 which primarily converted to rice-based systems. Presently degraded mangrove and adjacent rice ecology in Sundarban-India placed side by side and create typical ecology which is distinct in nature in respect to soil physicochemical properties, carbon dynamics, and microbial diversities. We investigated the structural and functional diversities of bacteria and archaea through Illumina MiSeq metagenomic analysis using V3–V4 region of 16S rRNA gene approach that drives greenhouse gases emission and carbon-pools. Remote sensing-data base were used to select the sites for collecting the soil and gas samples. The methane and nitrous oxide emissions were lower in mangrove (−0.04 mg m−2 h−1 and −52.8 μg m−2 h−1) than rice (0.26 mg m−2 h−1 and 44.7 μg m−2 h−1) due to less availability of carbon-substrates and higher sulphate availability (85.8% more than rice). The soil labile carbon-pools were more in mangrove, but lower microbial activities were noticed due to stress conditions. A unique microbial feature indicated by higher methanotrophs: methanogens (11.2), sulphur reducing bacteria (SRB): methanogens (93.2) ratios and lower functional diversity (7.5%) in mangrove than rice. These could be the key drivers of lower global warming potential (GWP) in mangrove that make it a green production system. Therefore, labile carbon build-up potential (38%) with less GWP (63%) even in degraded-mangrove makes it a clean production system than wetland-rice that has high potential to climate change mitigation. The whole genome metagenomic analysis would be the future research priority to identify the predominant enzymatic pathways which govern the methanogenesis and methanotrophy in this system.

Playing by the rules? Phenotypic adaptation to temperate environments in an American marsupial.

Phenotypic variation along environmental gradients can provide evidence suggesting local adaptation has shaped observed morphological disparities. These differences, in traits such as body and extremity size, as well as skin and coat pigmentation, may affect the overall fitness of individuals in their environments. The Virginia opossum (Didelphis virginiana) is a marsupial that shows phenotypic variation across its range, one that has recently expanded into temperate environments. It is unknown, however, whether the variation observed in the species fits adaptive ecogeographic patterns, or if phenotypic change is associated with any environmental factors. Using phenotypic measurements of over 300 museum specimens of Virginia opossum, collected throughout its distribution range, we applied regression analysis to determine if phenotypes change along a latitudinal gradient. Then, using predictors from remote-sensing databases and a random forest algorithm, we tested environmental models to find the most important variables driving the phenotypic variation. We found that despite the recent expansion into temperate environments, the phenotypic variation in the Virginia opossum follows a latitudinal gradient fitting three adaptive ecogeographic patterns codified under Bergmann’s, Allen’s and Gloger’s rules. Temperature seasonality was an important predictor of body size variation, with larger opossums occurring at high latitudes with more seasonal environments. Annual mean temperature predicted important variation in extremity size, with smaller extremities found in northern populations. Finally, we found that precipitation and temperature seasonality as well as low temperatures were strong environmental predictors of skin and coat pigmentation variation; darker opossums are distributed at low latitudes in warmer environments with higher precipitation seasonality. These results indicate that the adaptive mechanisms underlying the variation in body size, extremity size and pigmentation are related to the resource seasonality, heat conservation, and pathogen-resistance hypotheses, respectively. Our findings suggest that marsupials may be highly susceptible to environmental changes, and in the case of the Virginia opossum, the drastic phenotypic evolution in northern populations may have arisen rapidly, facilitating the colonization of seasonal and colder habitats of temperate North America.

Multiview Canonical Correlation Analysis Networks for Remote Sensing Image Recognition

In the past decade, deep learning (DL) algorithms have been widely used for remote sensing (RS) image recognition tasks. As the most typical DL model, convolutional neural networks (CNNs) achieves outstand performance for big RS data classification. Recently, a variant of CNN, dubbed canonical correlation analysis network (CCANet), was proposed to abstract the two-view image features. Extensive experiments conducted on several benchmark databases validate the effectiveness of CCANet. However, the CCANet structure is powerless when the observations arrive from more than two sources. To serve the multiview purpose, in this letter, we propose multiview CCANets (MCCANets). Particularly, the MCCANet model learns the stacked multiperspective filter banks by the MCCA method and builds a deep convolutional structure. In the output stage, the binarization and the blockwise histogram are employed as nonlinear processing and feature pooling, respectively. To access the effectiveness of the MCCANet, we conduct a host of experiments on the RSSCN7 RS database. Extensive experimental results demonstrate that the MCCANet outperforms the two-view CCANet.

Determining Spatial Parameters of the Ecological Niche of Parus Major (Passeriformes, Paridae) on the Base of Remote Sensing Data

Abstract Using factor analysis of ecological niches, we found that Parus major has high marginality to such ecogeographical variables (EGVs), as normalized difference vegetation index, the altitude above sea level, the diffuse insolation, activity of chlorophyll, normalized difference water index. This species is highly specialized in relation to various vegetation indices. Based on the type of habitat preference map, we found that Parus major doesn’t implement all its potential pro-spatial niche. Considering the ecological niche of great tit on different levels of scale, we noticed certain features: first, a list of factors that influence the distribution of great tit significantly altered by changing the scale, secondly, the factors that play a significant role in spreading Parus major on level of total consideration losing their weight and relevance on closer inspection (when the scale down); third, although specialization of great tits changes with the scale of consideration but Parus major mostly specialized by vegetation index.

Supercooled Liquid Water In Winter Storms: A Preliminary Climatology from Remote Sensing Observations

Drawing from a remote sensing data base obtained from winter storm research programs in Colorado and Utah, the occurrence of supercooled liquid water over mountain barriers is examined. Combined polarization lidar and dual-channel microwave radiometer data reveal that liquid water was nearly always presenting the storms studied. Moreover, the highest frequency of occurrence and liquid water amounts were most often associated with relatively warm cloud base temperatures and Ku-band radar reflectivity factors between 0 and -10 dBZ at the liquid cloud base position. A preliminary climatology of super cooled liquid water in southern Utah reveals a bimodal distribution of liquid cloud base heights, representing of convective clouds (-3.0 km MSL) and generally prefrontal stratiform clouds (~4.5 km MSL). Although the liquid water associated with the efficient natural generation of precipitation may not always be detected by any single probe, the conditions which could be expected to yield a favorable seeding response can be identified through joint remote sensing observations.

Evaluation of the role of submerged plant beds in the metal budget of a fluvial lake

In a study of a large fluvial lake (Lake St. Pierre, St. Lawrence River system, Quebec), we have determined the biomass of the submerged vegetation (dominated by Vallisneria americana and Potamogeton spp) during the peak seasonal biomass (August) and senescence (October), and have estimated the content of Cd, Cr, Cu, Ni, Pb and Zn in the above-ground portions of these plants. Multispectral remote sensing data (MEIS-II) were used to extrapolate from point measures of biomass (g m−2) to the entire area of the lake (Lavoie et al., 1991). By combining field information (biomass values and metal concentrations) with the more extensive remote sensing data base of biomass values, and by using geostatistical estimation techniques (kriging), we have estimated the seasonal storage of metals in Lake St. Pierre plants to be: 30 kg Cd, 89 kg Cr, 450 kg Cu, 280 kg Ni, 71 kg Pb and 2200 kg Zn. During the seasonal biomass peak, the quantities of Cd, Pb and Zn stored in the plants were higher than those dissolved in the water column, but much lower than those present in the surficial, recent sediments. Mass balance calculations for the summer months indicated that the ‘macrophyte’ compartment represented only a small proportion of the metals entering the lake: Cu and Ni, <1%; Cd and Zn, 2%; Pb, 4%. Senescence and the downstream drifting of plant material noted in October suggested that most of the metals associated with the above-ground parts of the submerged vegetation were not recycled within the lake, but instead were exported at the end of the summer.

The decrease of lake Chad as documented during twenty years of manned space flight

Space photography has been successfully used to extend the space remote sensing data base for environmental monitoring by a decade. In this study of Lake Chad, space photographs were digitized and registered to a topographic base map before water classifications were performed. From 1966 to 1985 over a 21,000-square-kilometer decrease in lake surface area was observed.