Moderate Spatial Resolution Research Articles

Improvements in high-temporal resolution active fire detection and FRP retrieval over the Americas using GOES-16 ABI with the geostationary Fire Thermal Anomaly (FTA) algorithm

Geostationary imaging sensors offer unique high temporal resolution capabilities with which to characterise the fast-changing dynamics of landscape fires. The new R-Series of Geostationary Operational Environmental Satellite (GOES-R) are the most advanced geostationary weather satellites currently operating, and each carry the new Advanced Baseline Imager (ABI) which greatly improves on the spatial, temporal and radiometric characteristics of the previous GOES Imager. Here we develop and evaluate the first landscape fire radiative power (FRP) observation system for the first of the GOES-R series (GOES-16), comparing its outputs to those generated near simultaneously from the forerunner GOES-13 Imager and from the far higher spatial resolution, but far less frequently imaging, Moderate Spatial Resolution Imaging Spectroradiometer (MODIS). Across the Americas, when examining near-simultaneous data from August to September 2017, the geostationary Fire Thermal Anomaly (FTA) algorithm originally developed for use with Meteosat’s Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) enables GOES-16 ABI to detect substantially more (6%) active fire (AF) pixels than GOES-13 Imager, due mainly to the former sensors‘ smaller pixel area. ABI detects even greater numbers of AF pixels due to its higher temporal resolution, but here we are only comparing results from near-simultaneous imagery. The majority of fires having an FRP substantially less than ~30 ​MW remain undetected even by ABI, and since these are the most common fire type ABI still shows a high error of omission (68%) compared to MODIS centre of swath data (±30° scan angle). ABI AF pixel errors of commission are far lower than errors of omission however at ~12% when using MODIS as a benchmark. This appears far more favorable than results found when using the Fire Detection and Characterization (FDC) algorithm, an adaptation of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm long-applied to GOES, which when applied to ABI data was recently assessed to have an 88% commission error. When ABI and MODIS both successfully detect a fire at the same time there is strong agreement and low bias in terms of their retrieved FRP. Overall therefore we find that the FTA approach to generating AF data from GOES-16 ABI sensor appears well performing across the Americas, and is compatible with the data from this system joining that from Meteosat, Meteosat over India Ocean and Himawari-8 to become part of the global geostationary active fire observation system now reaching maturity. The AF detection and FRP data provided from GOES-16 operating as GOES-East, and also now GOES-17 operating from the GOES-West position, are now available in near real-time for use worldwide.

Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures by combining meteorological data and satellite imagery

AbstractAboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat‐8 and Sentinel‐2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs—normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)—and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry‐matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil.

Assessing vegetation restoration potential under different land uses and climatic classes in northeast Iran

This study examines the trends in vegetation cover using the Growing Season NDVI (GSN) time series in moderate spatial resolution (250 m) over Khorasan Razavi province, in northeast Iran, during the period 2004–2015. The province is largely desert, with extra-arid, arid, and semi-arid de Martonne climate zones dominating, while rangelands, shrublands and deserts cover most areas, making it an ideal territory for monitoring vegetation trends and implement future restoration projects. Most parts of the province and land-cover classes show no trends in vegetation cover, but large decreasing trends occur in areas covered by sand dunes, previously reforested lands and clay pit areas. Trends in various land-cover types are also examined as functions of the climatic class and the Terrain Niche index (TNI), which is characteristic of the topography, revealing large decreasing trends in the extra-arid climatic zone. In addition, most of the areas exhibit Hurst exponent values around 0.5, implying stochastic time series without any consistency and a likelihood of random vegetation and land cover changes in the future. This study also aims to determine likely future vegetation status and the most favourable areas for restoration projects through analysis of two indexes (Future Restoration Dispersal Index, FRDI and Future Uncertainty Dispersal Index, FUDI). The results show that reforestation, sand dunes and clay pits areas are the most favourable for implementing restoration projects, while the spatial distribution of the potential restoration classes reveals that the southern and northeastern parts of Khorasan Razavi province are the most favourable areas for establishing environmental restoration activities in order to avoid further degradation of ecosystems.

Исследование антропогенных изменений в городском ландшафте по данным инфракрасного диапазона Landsat-8

Changes in the urban landscape can be detected using visible and far infrared remote sensing. Thermal imagery is widely used for research and monitoring of natural and anthropogenic objects. Multiple data sources use enhances spatial analysis capabilities. These sources are, for example, MODIS satellite data with moderate spatial resolution and daily surveys, Landsat data collection with medium spatial resolution and survey frequency of about once every 14 days, and high-resolution PlanetScope data with the ability to acquire multiple scenes per day. This paper proposes the methodology for identifying anthropogenic transformations in the urban landscape by land surface temperature. Three categories of changes were identified: no changes, minor changes with a temperature difference of 1–3 ºC and significant changes with a temperature difference of more than 4 ºC. When analyzing areas with maximum temperature changes, it was noted that the dynamics of changes are associated with changes in urban development, namely, with the construction of new microdistricts and shopping centers. A change in surface temperature is also associated with a change in forest landscapes, for example, in places where tree plantations were cut down.

The methods and automatic technology aimed at imagery georeferencing, cloud screening, atmospheric and radiometric correction of KMSS-M satellite data

In this study we present methods and automatic technology developed for routine processing of satellite imagery acquired by cameras MSU-201 and MSU-202 (KMSS-M) onboard Meteor-M №2. The developed methods were aimed at imagery georeferencing issues fixing, clouds and shadows detection as well as atmospheric and radiometric correction. Basing on these methods we built an automatic technology and complete KMSS-M data processing chain which provided analysis ready dataset for Russian grain belt and adjacent areas of neighboring countries for the year 2020. Method for imagery georeferencing was based on Pearson’s correlation localized maximization when compared to the georefenced and cloudfree coarse-resolution reference image produced in IKI RAS through MOD09 product time series processing. Method for clouds and shadows detection was based both on the spatial analysis of outputs from geocorrection step and auxiliary image, characterizing georeferenced KMSS-M image values relative accordance with the IKI reference image. The atmospheric correction was based on localized histogram matching of KMSS-M and IKI reference date-corresponding imagery, and thereby concurrently performed radiometric correction of KMSS-M data, compensating effects of varying viewing and illumination geometry which explicitly manifest across 960-km-wide swath area. The developed methods are noticeably minimalistic, requiring only one target spectral band to perform properly. Due to high flexibility and robustness, they also may be applied to raw satellite imagery acquired from various Earth observation systems, including Russian systems of high and moderate spatial resolution. The technology is currently being deployed in an operative mode for several test sites of Russia since the year 2021 onwards.

MONITORING OF EMERGENCY SITUATIONS AT OIL AND GAS FACILITIES

The paper briefly presents a methodological approach to processing multi-zone satellite images of moderate spatial resolution. This approach allows detecting oil spill sites with sufficient reliability during rapid response and subsequent liquidation of the consequences of man-made emergencies at oil and gas facilities. The properties of modern, often used for monitoring territories, space remote sensing tools are analyzed in relation to the monitoring of man-made emergencies. The object of the study is the vicinity of the CHPP 3 of the Norilsk-Taimyr Energy Company, where a major accident occurred on May 29, 2020 – a tank with diesel fuel was depressurized at the facility – and as a result of the spill, more than 20 tons of fuel fell into the waters of the Daldykan and Ambarnaya rivers. This is one of the largest leaks of petroleum products in the Arctic zone in history, posing a threat to the ecosystem of the Arctic Ocean. Processing and subsequent analysis of satellite images of the territory of interest and surrounding objects were made during the research. According to satellite images, it became possible to assess the scale of the environmental disaster.

Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks

Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects.

Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps

Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land use (LCLU) information that could be used to expand the historic record when combined with data from moderate spatial resolution Earth observation missions. This is especially true for landscape disturbances that have a long and complex historic record, such as surface coal mining in the Appalachian region of the eastern United States. In this study, we investigate this specific mapping problem using modified UNet semantic segmentation deep learning (DL), which is based on convolutional neural networks (CNNs), and a large example dataset of historic surface mine disturbance extents from the USGS Geology, Geophysics, and Geochemistry Science Center (GGGSC). The primary objectives of this study are to (1) evaluate model generalization to new geographic extents and topographic maps and (2) to assess the impact of training sample size, or the number of manually interpreted topographic maps, on model performance. Using data from the state of Kentucky, our findings suggest that DL semantic segmentation can detect surface mine disturbance features from topographic maps with a high level of accuracy (Dice coefficient = 0.902) and relatively balanced omission and commission error rates (Precision = 0.891, Recall = 0.917). When the model is applied to new topographic maps in Ohio and Virginia to assess generalization, model performance decreases; however, performance is still strong (Ohio Dice coefficient = 0.837 and Virginia Dice coefficient = 0.763). Further, when reducing the number of topographic maps used to derive training image chips from 84 to 15, model performance was only slightly reduced, suggesting that models that generalize well to new data and geographic extents may not require a large training set. We suggest the incorporation of DL semantic segmentation methods into applied workflows to decrease manual digitizing labor requirements and call for additional research associated with applying semantic segmentation methods to alternative cartographic representations to supplement research focused on multispectral image analysis and classification.

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

Development of X-ray phase CT with a hybrid configuration of Lau and Talbot-Lau interferometers.

X-ray phase computed tomography (CT) is used to observe the inside of light materials. In this paper, we report a new study to develop and test a laboratory assembled X-ray phase CT system that comprises an X-ray Lau interferometer, a rotating Mo anode X-ray tube, and a detector with high spatial resolution. The system has a high spatial resolution lower than 10 μm, which is evaluated by differentiating neighbouring carbon fibres in a polymer composite material. The density resolution is approximately 0.035 g/cm3, which enables to successfully distinguish the high-density polyethylene (HDPE, 0.93 g/cm3) from the ultra-low-density polyethylene (ULDPE, 0.88 g/cm3) in the sample. Moreover, the system can be switched to operate on another mode based on a Talbot-Lau interferometer that provides a wider field of view with a moderate spatial resolution (approximately 100 μm). By analyzing sample images of the biological, this study demonstrates the feasibility and advantages of using hybrid configuration of this X-ray phase CT system.

Internetwork Magnetic Fields Seen in Fe i 1564.8 nm Full-disk Images

Abstract We studied the properties of internetwork magnetic fields in the solar photosphere, taking advantage of full-disk Stokes V/I maps of the Fe i 1564.8 nm line, which were obtained during 2010–2019. In contrast to most previous studies, we used data with moderate spatial and spectral resolutions. Nonetheless, we were able to distinguish the internetwork field components and the active region/network boundary components using large Zeeman splitting of the Fe i 1564.8 nm line. Thus, our analysis provides a point of view quite different from that of previous studies. We analyzed the data statistically without ordinary inversions, yet we successfully derived some properties of internetwork fields; the internetwork is filled with small-scale magnetic fields, their strength is within the weak field regime of the Fe i 1564.8 nm line (300–400 G or less), and the internetwork fields are highly inclined. Although the results were obtained from the analysis performed from a different perspective, they are consistent with the majority of previous findings. In addition, no notable variation in the properties of the internetwork fields was found during the period covering most of solar cycle 24.

An experimental sky-image-derived cloud validation dataset for Sentinel-2 and Landsat 8 satellites over NASA GSFC

Availability of a reliable cloud mask for optical satellite imagery is a prerequisite, when generating high-quality high-level geoinformation products. Creation of a reference (ground truth) cloud mask for moderate spatial resolution sensors, such as Operational Land Imager (OLI) aboard Landsat 8 and Multispectral Instrument (MSI) aboard Sentinel-2A/B satellites, is a challenging and time-consuming task. Existing reference datasets were mainly produced through photointerpretation of satellite images by an analyst, which can introduce subjectivity in detecting clouds. Therefore, other methods for generating cloud reference data shall be explored and evaluated that can complement existing datasets. In this paper, we document generation and provide the description of a new reference cloud dataset, named GSFC-Cloud, which is based on the extensive use of ground-based images of the sky. The dataset is collected over the same area, covers various cloud conditions, and is available for six Landsat 8 and twenty-eight Sentinel-2 scenes spanning the period of September 2017 to November 2018. The dataset is available in the vector format, so cloud masks at various spatial resolutions can be validated. We also describe a system to automate the process of ground-based data collection using low-cost off-the-shelf parts with the long-term objective to replicate this set-up in multiple locations around the world. We use the proposed dataset to validate and improve the Land Surface Reflectance Code (LaSRC) for cloud detection in Sentinel-2 imagery. We show that adding a parallax feature to estimate a subpixel shift between red and green bands with a phase correlation method can reduce overdetection of clouds and improve performance of LaSRC.

Crop residue burning practices across north India inferred from household survey data: Bridging gaps in satellite observations

Abstract In north India, agricultural burning adversely affects local and regional air quality during the post-monsoon season (October to November), when the prevailing meteorology is favorable for smog and haze formation. Quantifying the contribution of smoke to air pollution in this region, however, is challenging. While the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard NASA's Terra and Aqua satellites, provides a nearly 20-year record of global fire activity, the sensor cannot adequately capture small, short-lasting agricultural fires due to its moderate spatial resolution (500 m–1 km) and limited overpasses (twice daily for each satellite), as well as the hazy conditions that typically obscure the north India land surface at this time of year. Moreover, current global fire emissions inventories based on MODIS observations can differ by up to an order of magnitude in this region. Here we incorporate household survey data to bridge gaps in MODIS observations and improve estimates of fire emissions over the states of Punjab, Haryana, Uttar Pradesh, and Bihar during the 2003–2018 post-monsoon burning seasons. We develop a novel method that adjusts MODIS Fire Radiative Power (FRP) for: (1) small fires detected by the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375-m spatial resolution, (2) cloud/haze gaps in satellite observations, (3) partial-field burning practices, and (4) the diurnal cycle of fire activity. Adjusting FRP for the fire diurnal cycle yields the largest boost to emissions due to the short lifetime of the fires (~1/2 h) and the brief windows of satellite detection. Using adjusted FRP, we estimate on average 10.4 ± 3 Tg dry matter (DM) burned each year, yielding emissions of 65 ± 18 Gg organic carbon (OC), 5.6 ± 1.6 Gg black carbon (BC), 791 ± 225 Gg CO, and 14.9 ± 4.2 Tg CO2. On average, our OC + BC emissions are 3.4 times (min: 0.6, max: 6.6) the estimates from five widely used global fire emissions inventories. Our estimate for Punjab, which contributes more than two-thirds of emissions in the region, is consistent with our bottom-up validation, which uses burn rates from the household survey and government crop production statistics in 2016 and 2017. We spatially disaggregate the state-level emissions to construct a gridded inventory at daily, 0.25 ° × 0.25 ° resolution over north India from 2003 to 2018. The inventory, SAGE-IGP (Survey Constraints on FRP-based Agricultural Fire Emissions in the Indo-Gangetic Plain), improves modeling assessments of air quality impacts from agricultural burning, thus supporting effective policy development.

Long-term trends in anthropogenic land use in Siberia and the Russian Far East: a case study synthesis from Landsat

As globally important forested areas situated in a context of dramatic socio-economic changes, Siberia and the Russian Far East (RFE) are important regions to monitor for anthropogenic land-use trends. Therefore, we compiled decadal Landsat-derived land-cover and land-use data for eight dominantly rural case study sites in these regions and focused on trends associated with settlements, agriculture, logging, and roads 1975–2010. Several key spatial–temporal trends emerged from the integrated landscape-scale analyses. First, road building increased in all case study sites over the 35-year period, despite widespread socio-economic decline post-1990. Second, increase in settlements area was negligible over all sites. Third, increased road building, largely of minor roads, was especially high in more rugged and remote RFE case study sites not associated with greater agriculture extent or settlement densities. High demands for wood export coupled with the expansion of commercial timber harvest leases starting in the mid-1990s are likely among leading reasons for an increase in roads. Fourth, although fire was the dominant disturbance over all sites and dates combined, logging exerted a strong land-use pattern, serving as a reminder that considering local anthropogenic landscapes is important, especially in Siberia and the RFE, which represent almost 10% of the Earth’s terrestrial land surface. The paper concludes by identifying remaining research needs regarding anthropogenic land use in the region: more frequent moderate spatial resolution imagery and greater access to more finely resolved statistical and other spatial data will enable further research.Social media abstractLandsat reveals long-term anthropogenic land-use trends in Siberia and Russian Far East

Application of Satellite Remote Sensing in Monitoring Elevated Internal Temperatures of Landfills

Subsurface fires and smoldering events at landfills can present serious health hazards and threats to the environment. These fires are much more costly and difficult to extinguish than open fires at the landfill surface. The initiation of a subsurface fire may go unnoticed for a long period of time and undetected fires may spread over a large area. Unfortunately, not all landfill operators keep or publish heat elevation data and many landfills are not equipped with a landfill gas extraction system to control subsurface temperatures generated from the chemical reactions within. The timely and cost-effective identification of subsurface fires is an important and pressing issue. In this work, we describe a method for using satellite thermal infrared imagery at a moderate spatial resolution to identify the locations of subsurface fires and monitor their migration within landfills. The focus of this study was the Bridgeton Sanitary Landfill in Bridgeton, MO, USA where a subsurface fire was first identified in 2010 and continues to burn today. Observations from Landsat satellites over the last seventeen years were examined for surface temperature anomalies (or hot spots) that may be associated with subsurface fires. The results showed that the locations of hot spots identified in satellite imagery match the known locations of the subsurface fires. Changes in the hot-spot locations with time, as determined by in situ measurements, correspond to the spreading routes of the subsurface fires. These results indicate that the proposed approach based on satellite observations can be used as a tool for the identification of landfill subsurface fires by landfill owners/operators to monitor landfills and minimize the expenses associated with extinguishing landfill fires.

MULTI-SCALE ANALYSIS OF JACK PINE SAPLINGS AFTER FIRE ACROSS BURN SEVERITIES

Abstract. Fire serves as a successional initiation in jack pine (Pinus banksiana) forests of North America, as jack pine reproduce using seratonous cones that open only in intense heat. Jack pine seedling resilience after fire is characterized by high numbers of mortality. The estimation of sapling survivability and density is useful for understanding dynamics of carbon sequestration, forest structure and dynamic, and supporting management of the landscape. Most studies concerning the interaction of forest disturbances occurs at moderate spatial resolution. These moderate resolution data analyses do not adequately capture the fine scale spatial variation of the landscape after fire for understanding sapling survival. Thus, high-resolution data, such as aerial photography may provide more detailed information to support decision-making. A key to the types of spatial patterns that emerge in these early years is the pre-fire stand condition. In heavily managed areas, the mosaic of forest patches may include extensive variety in disturbance conditions. In this current research we address the problem of scale in relation to understanding the influence of pre-fire condition on post-fire early recovery patterns. To do this, we combine data output from the LandTrendr algorithm in Google Earth Engine with spectral data from aerial photography collected by airplane and Unmanned Aerial System to perform a random forest classification. The result is a finer scale resolution map of forest conditions of varying sapling density.

Super-resolution mapping of hyperspectral data using Artificial Neural Network and wavelet

Hyperspectral images are characterized by high spectral but moderate spatial resolution and are therefore contains mixed pixels. Hyperspectral is capable of detecting the type of material represented by endmembers by utilizing the unique absorption dip in the reflected wavelength spectra. The quantification of present endmembers in mixed pixel has been investigated widely in the recent times. This quantification is known as Hyperspectral unmixing which also refers to the decomposition of these mixed pixels into the set of endmembers and abundance fractions. Estimation of abundance fraction has often been solved based on the linear mixing model because of their simplicity while on many occasions non-linear spectral unmixing has been utilized to enhance the simulation of spectral mixing. In this study, we have proposed a novel approach (cum algorithm) to super resolved the fractional images (generated through spectral unmixing). We have used Artificial Neural Network (ANN) coupled with wavelet, that decomposes the images into different level of frequencies. These frequency components are further fed to ANN to learn the network. A learned network is then used to predict coarser and finer features separately thereby preserving the smaller features and produces a better representation of higher resolution map. The accuracy of the super-resolved map was compared using a high resolution image. Object characterization is adopted for metric accuracy measure and positional accuracy for spatial consistency. We also performed metric comparison for few features in original image, high resolution image (obtained from Google Earth image at best resolution) and super-resolved image. We found the algorithm is performing very well for comparatively larger features and satisfactory for others. We also found the very good metric accuracy and positional accuracy to represent the features in super-resolved images. The results shows, features are better represented in super-resolved image than original image when compared their dimensions with high resolution Google Earth image. Since we have degraded the original image to collect the training data so this approach may not be suitable for small features however performing very well for relatively bigger features.

Integration of statistical forest reflectance model and Sentinel-2 MSI images into a continuous forest inventory system

Spectral signatures of forest stands in Sentinel-2 MSI spectral bands are simulated with the statistical forest reflectance (SFRM) model and compared to the spectral signatures measured in spectral images at ten study sites in Estonia. As an overall measure of the agreement between simulated and measured spectral signatures we used the total error calculated as the sum of relative errors over spectral bands B2 to B11 of Sentinel-2. The distribution of the total error has strongly positive skewness at all study sites and all types of forests (broadleaf, pine and spruce forests). The right tail of the distribution is low. The stands of high value of the total error far right in the tail of the distribution may have some errors in their inventory data, or the inventory data are outdated. Pertinent stands should have priority in their in situ checking process. The SFRM model is a simple and reliable tool for the validity checking of forest inventory data, using routinely collected forest inventory data and operational satellite information of moderate spatial resolution. The model is simple and computationally efficient. Preparing input data for the model is a simple query in the forest inventory database. The suggested procedure can be incorporated into the automated systems of continuous forest inventory. 
 
 Keywords: Forest inventory, Sentinel-2 MSI images, Statistical forest reflectance model

Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy

Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.

Spatio-temporal assessment of Aerosol Optical Depth from Himawari-8 satellite data over Malaysia

Aerosols, particulate matters and air pollution are significant atmospheric issues in the Southeast Asia (SEA) countries particularly Malaysia. Despite their importance, data to investigate their spatio-temporal variation, estimating their abundance and modelling the trend are not widely available due to financial constraints and technical expertise. Remote sensing data are alternative solution to the limited data available from ground stations. One of the latest remotely sensed Aerosol Optical depth (AOD) data available is from Himawari-8 satellite that provides high temporal resolution data (every 10 minutes) at moderate spatial resolution of 5 km. Nevertheless, the robustness of these data for the tropical region has not yet been validated yet. This study aimed at assessing the performance of Himawari-8 AOD L2 product by (i) comparing its absolute AOD values against AOD retrieved from AERONET stations and (ii) analysing the spatial and temporal patterns of the AOD data. The AERONET data were averaged for ±5 min of the Himawari-8 overpass and matched to each other using statistical measures i.e. Correlation, Root mean square error (RMSE), Relative bias (RB) and Mean Absolute Error (MAE). Preliminary results of the analysis provided a coefficient of determination (R2) value = 0.31, RMSE = 0.16, and MAE = 0.15. The Himawari-8 AOD product is found to be overestimated the AOD values by 24.19%. Nevertheless, Himawari-8 AOD L2 product is able to exhibit similar spatial and temporal patterns compared to other remotely sensed AOD data such as that of MODIS. The high temporal resolution of Himawari data will facilitate frequent monitoring of air pollution in this region.