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The difficulty in identifying collapsed houses and damaged structures in synthetic aperture radar (SAR) images after natural disasters represents a significant challenge in the monitoring of urban structural deformation using SAR. SAR image simulation was conducted on a three-dimensional model of a typical wooden building in Japan to analyze the scattering mechanism of the structure in collapsed and uncollapsed states. Based on the physical properties of the buildings, a correlation was established between the simulated SAR image feature signals and the geometric structures of the buildings. The findings indicate that SAR scattering is more uniform for uncollapsed structures, which is predominantly influenced by their geometry. At low incidence angles, single reflections were the predominant phenomenon, whereas at high incidence angles, multiple reflections became more prevalent. The uncollapsed building’s facade formed a dihedral angle, exhibiting bright lines in the SAR image. Multiple reflections occurred at the edges of the building and floor junctions. These findings follow the theoretical predictions. In the case of the collapsed buildings, multiple reflections occurred with greater frequency, and irregular scattering was observed. Notwithstanding the augmented scattering pathways, some walls nevertheless manifested single reflections. The collapsed structures demonstrated a reduced sensitivity to alterations in the angle of incidence.

“Lifeline of the nation” is the motto of Indian Railways as it connects through a common thread, billion plus population in one way or the other. The National Rail Plan for India – 2030 focuses on creating a ‘future ready’ Railway system by 2030 by suitably integrating new railway systems like high-speed rails. However, rail infrastructure is exposed to multi-hazards and disasters sometimes disrupt safe rail operations. This study explores rail infrastructure risk assessment at a national scale utilizing the UNDRR framework and synthesized application of geospatial technologies with a focus on disentanglement of local vulnerabilities of the rail infrastructure assets utilizing factors of health of bridges, visibility obstruction to level crossings, labour wages & their regions and GSDP under multi-hazard scenarios. The results revealed that the NR and NFR were identified as high-risk routes under the risk analysis of physical and social vulnerability scenarios, followed by CR Railways. The average annual frequencies of emergency cases in each zone show a correlation r (17) = 0.4758 with the combined mean risk ranks for each zone. In comparison to socioeconomic factors, which contribute to indirect losses, physical factors directly affect safety and contribute to direct losses. Further, outcomes depict more accidents on Indian Railways during the monsoon (nearly 50%) and cold weather (29%) seasons. The study suggests that with the participation of key stakeholders, including urban and transport planners, an integrated approach is helpful in identifying critical rail routes towards risk-informed adaptive disaster-resilient infrastructure planning for providing safety, continuity and reliability of essential rail services.

Abstract This study compared the time profile of FEV1 after COPD diagnosis among rapid decliners, slow decliners, and sustainers in the year of COPD diagnosis. COPD subjects were identified from the annual medical checkup records of Hitachi, Ltd., employees in Japan (April 1998-March 2019). Subjects were categorized into 3 groups (rapid decliner [decrease of FEV1 ≥ 63 mL/year], slow decliner [

Seagrass ecosystems are crucial to the carbon cycle, biodiversity, fisheries, and erosion prevention. However, there are gaps in mapping seagrass in tropical coastal waters due to turbidity and constantly changing tidal dynamics. To tackle such issues, tidal heights and reflectance of 33 Sentinel-2 images were used to calculate a multitemporal composite of Sentinel-2 images, which was classified using the Random Forest classifier. UAV images classified with Object-based image analysis provided ground truth for training and validation. The effectiveness of water column correction and using the multitemporal composite was evaluated by comparing overall classification accuracy. As a result, applying water column correction improved classification accuracy from 80.4% to 80.6%, while using the multi-temporal composite improved accuracy to 88.6%, among the highest accuracy achieved for seagrass classification in turbid waters. The combined protocol could lead to advances in quantifying seagrass distribution in the tropical coastal waters and their carbon sequestration capacity, resolving significant uncertainties in blue carbon estimation.

Abstract Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

Abstract Building alterations often occur when the buildings ought to be updated to meet new standards. In such processes, original values of existing buildings tend to be neglected in favor of speed and economic benefits from aggressive alterations. In this research, we investigate the mechanism of architectural alterations that respect the value of existing buildings. The research helps uncover the lasting values of buildings that can be preserved in spite of the need for physical adjustments.

Abstract COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25–April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

Solar photovoltaic (PV) technology has been widely used as a major source of renewable energy. Vietnam is especially active in installing solar energy systems. The total installed solar PV capacity in Vietnam has significantly increased since 2019, but the spatial evolution of solar panels is yet to be discussed. Therefore, this study aims to reveal the shift that occurred in solar photovoltaic planning sites in Vietnam from 2019 to 2022. Solar PV maps were produced from Sentinel-2 imagery via a deep learning segmentation model. Land cover maps, terrain slope, solar power potential, population density, and power grid datasets were compared to the locations of the detected PV sites each year to reveal a shift in the solar farm planning sites. The result show that the deep learning model achieved satisfactory performance. The observed shift in the PV installation sites suggests that for the first two years, large solar farms were built on suitable land near the electricity grid, while smaller PVs were constructed at locations less suitable for solar energy production in 2021 and 2022. These findings suggest that the shift in solar PV planning in Vietnam was caused by the availability of suitable land with an appropriate energy transfer capacity and the participation of smaller-scale PV operators.

Inspection data can be used to comprehend and plan effective maintenance of bridges. In particular, the year of initial construction is one of the most important criteria for formulating maintenance plans, making budget allocations, and estimating soundness. In an initial survey of bridges in Cambodia, it was concluded that the year of construction of only 54% of 2439 bridges surveyed is known, with the remaining 46% remaining unknown. In this research, Landsat satellite data is used to estimate the year of construction of these bridges. Landsat provides spatial spectral reflectance information covering more than 30 years, and for longer bridges this can be used to estimate the year of construction by visual judgement. However, limited image resolution means this is not possible for shorter bridges. Instead, a method using the Landsat Normalized Difference Water Index (NDWI) is used to estimate the year of construction. Three pixels are selected from Landsat image data in such a way that one lies on the current location of a bridge and two other reference pixels are placed on similar terrain at a certain distance perpendicular to the bridge axis. NDWI values are plotted over time for the three pixels and the difference in value between the bridge pixel and the two reference pixels is then compared. Before the bridge is constructed, all three pixels should have similar NDWI values, but after construction the value of the target bridge pixel should differ from the other two because the NDWI value of a bridge surface is different from that of the surrounding vegetation. By looking for this change, the year of construction of a bridge can be estimated. All the bridges in the Cambodian database are classified into three categories based on length (which affects their visibility in Landsat images) and year of construction is estimated. The results show that estimated year of construction has the same accuracy in all three categories.

Quantifying total carbon (TC) stocks in soil across various mangrove ecosystems is key to understanding the global carbon cycle to reduce greenhouse gas emissions. Estimating mangrove TC at a large scale remains challenging due to the difficulty and high cost of soil carbon measurements when the number of samples is high. In the present study, we investigated the capability of Sentinel-2 multispectral data together with a state-of-the-art machine learning (ML) technique, which is a combination of CatBoost regression (CBR) and a genetic algorithm (GA) for feature selection and optimization (the CBR-GA model) to estimate the mangrove soil C stocks across the mangrove ecosystems in North Vietnam. We used the field survey data collected from 177 soil cores. We compared the performance of the proposed model with those of the four ML algorithms, i.e., the extreme gradient boosting regression (XGBR), the light gradient boosting machine regression (LGBMR), the support vector regression (SVR), and the random forest regression (RFR) models. Our proposed model estimated the TC level in the soil as 35.06–166.83 Mg ha−1 (average = 92.27 Mg ha−1) with satisfactory accuracy (R2 = 0.665, RMSE = 18.41 Mg ha−1) and yielded the best prediction performance among all the ML techniques. We conclude that the Sentinel-2 data combined with the CBR-GA model can improve estimates of the mangrove TC at 10 m spatial resolution in tropical areas. The effectiveness of the proposed approach should be further evaluated for different mangrove soils of the other mangrove ecosystems in tropical and semi-tropical regions.

Mt. Bromo is geographically located in eastern of Java Island at 112°57'30'' longitude and 7°56' latitude, with a large area of caldera ~10 Km2. The land over the volcano is a perfect area for farming, one of the important factor affects the level of soil fertility is the most mineral rich soils. Volcanic activities at Mt. Bromo has been recorded in 1775 which characterized by small eruptions with cycles ranging from one to five years. Regarding this evidence, we tried to investigate the surface changes over the Mt. Bromo by using Time-Series InSAR with TimeFun algorithm. TimeFun is an extension of SBAS to allow incorporating various functions such as seasonal oscillations, polynomials, and step functions as generally it estimates DEM errors as well and allows missing observation. The maximum allowed baseline value is defined and used to constrain the interferogram pair by selecting manual after differential InSAR processing in single face working. The proposed analysis is based on 27 SAR data sets acquired by the ALOS/PALSAR sensors during the 2007–2017 time interval. The result shows us deformation occurred up to ~10 cm at summit of Mt. Bromo during the eruption period. Time-series monitoring of surface deformation to infer volume changes, geometries and locations of sources of deformation involved in the future eruption

The cultivation of Chinese cabbage is a crucial source of daily vegetable supply for both human consumption and livestock feed, particularly in East Asian countries. However, changes in global climate and land usage have resulted in significant shifts in the ecological conditions suitable for Chinese cabbage production, thereby threatening its productivity. To address this issue, this study was conducted to map the bio-geophysical suitability of Chinese cabbage in East Asia (Japan, Northeast China, South Korea, and North Korea) from 2001 to 2020. This study integrated six key factors—temperature, rainfall, photosynthetically active radiation (PAR), soil nitrogen, soil pH, and soil texture—into a seasonal and monthly bio-geophysical suitability assessment using a GIS-based Analytic Hierarchy Process–Multiple-Criteria Decision-Making Analysis (AHP-MCDA). The levels of bio-geophysical suitability were categorized into four levels: optimal, suitable, marginal, and unsuitable. The findings of the study firstly indicate that summer is the optimal season for Chinese cabbage cultivation, as it was found to have the highest level of optimal suitability among the four seasons in East Asia. South Korea has the largest percentage of optimal and suitable areas compared to the other three countries. Secondly, this study also conducted a comparison analysis between bio-geophysical suitability and Normalized Difference Vegetation Index (NDVI) over 20 years, and the results show good consistency between the two indicators, with the highest R2 value being 0.61. Thirdly, the comparison between bio-geophysical suitability and production data in two villages in Japan demonstrates that an increase in suitability from 0.28 to 0.32 indicates a significant increase in production. Production would stay stable even with further increases in suitability. Finally, two case studies with monthly comparisons of bio-geophysical suitability across Japan and East Asia in 2020 provide an effective benchmark for determining optimal sowing and harvest times. This study’s results can provide important insights into the trade of Chinese cabbage and support the development of agricultural insurance programs both for farmers and insurance companies. Furthermore, this approach may also be applicable for the assessment of the suitability of other crops.

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change.

BackgroundAirflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. ObjectiveThis study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database. MethodsIn this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV1) to prebronchodilator forced vital capacity (FVC) was

We observe the surface deformation of the Mt. Bromo which located at eastern Java Indonesia area that includes neighborhood volcanic system on TNBTS (Taman Nasional Bukit Tengger Semeru). Recently, remote sensing has played as an important role to observe volcano behaviour. We apply the SAR Interferometry (InSAR) algorithm referred to as Small Baseline Subset (SBAS) approach that allows us to generate mean deformation velocity maps and displacement time series for the studied area. The common SBAS technique, the set of interferometric phase observations writes as a linear combination of individual SAR scene phase values for each pixel independently. Particularly, the proposed analysis is based on 22 SAR data acquired by the ALOS/PALSAR sensors during the 2007–2011 time interval. A fewer studies have been able to show capability of InSAR analysis for investigating cycle of volcano especially of Mt. Bromo which characterized eruption stratovolcano in ranging one to five years. The results expected in this work represent an advancement of previous InSAR studies of the area that are mostly focused on the deformation affecting the caldera. According to the result, we expected this study could implement on risk management or infrastructure management.

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.

Abstract Background Hypoxic cancer cells are thought to be radioresistant and could impact local recurrence after radiotherapy (RT). One of the major hypoxic imaging modalities is [18F]fluoromisonidazole positron emission tomography (FMISO-PET). High FMISO uptake before RT could indicate radioresistant sites and might be associated with future local recurrence. The predictive value of FMISO-PET for intra-tumoral recurrence regions was evaluated using high-resolution semiconductor detectors in patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy (IMRT). Methods Nine patients with local recurrence and 12 patients without local recurrence for more than 3 years were included in this study. These patients received homogeneous and standard doses of radiation to the primary tumor irrespective of FMISO uptake. The FMISO-PET image before RT was examined via a voxel-based analysis, which focused on the relationship between the degree of FMISO uptake and recurrence region. Results In the pretreatment FMISO-PET images, the tumor-to-muscle ratio (TMR) of FMISO in the voxels of the tumor recurrence region was significantly higher than that of the non-recurrence region (p

Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abandoned agricultural lands, the identification of the spatial distribution of these lands must be acknowledged. While optical images had previously illustrated potentials in the identification of agricultural land abandonment, tropical areas often suffer cloud coverage problem that limits the availability of the imageries. Therefore, this study was conducted to investigate the potential of ALOS-1 and 2 (Advanced Land Observing Satellite-1 and 2) PALSAR (Phased Array L-band Synthetic Aperture Radar) images for the identification and classification of abandoned agricultural crop areas, namely paddy, rubber and oil palm fields. Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR; nonetheless, oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes. The accuracy obtained for these abandoned lands of paddy, rubber and oil palm was 93.33% ± 0.06%, 78% ± 2.32% and 63.33% ± 1.88%, respectively. This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops. The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

The shadow fraction of the canopy is an important factor in Bidirectional Reflectance Distribution Function (BRDF) and in estimating physical quantities, such as tree height and biomass. Shadows are used as a shielding ratio for direct solar irradiance, but, at shorter wavelengths, the amount of diffuse solar irradiance is greater, so the shielding ratio cannot be ignored. The shielding ratio of direct and diffuse solar irradiance is called Cast Shadow (CS) and Self Cast Shadow (SCS), respectively; however, it has been pointed out that the effect of these shadows is greater at higher resolutions, such as Sentinel-2. In addition, the Bottom Of Atmosphere (BOA) reflectance is greatly affected by shadows, because it is corrected for atmospheric effects. Therefore, the main purpose of this study is to investigate the spatial variability of CS and SCS and simulate the Sentinel-2BOA reflectance with these shadows. The target forest was a greenness season of a deciduous broadleaf forest in Thailand. First, we obtained a point cloud of the forest by Structure from Motion while using the Unmanned Aerial Vehicle. Next, we created a voxel model with CS and SCS as attributes. CS was calculated as the percentage of area where the plane that is assumed per voxel is shielded from direct solar irradiance by other voxels. SCS was calculated as the percentage of area where the hemispheric radiant environment is shielded by other voxels. Subsequently, using solar irradiance and leaf spectral reflectance data, the reflectance of each band of Sentinel-2 was simulated. Nine leaves were used to investigate the effect of leaf species on the simulation. The reflectance acquired by Sentinel-2 is not at the leaf level; however, we used this spectral reflectance data because the reflectance was simulated at the same spatial resolution as the voxel size. Voxel sizes of 20 cm, 50 cm, 100 cm, and 200 cm were used. Our result showed that (1) the spatial variability of SCS was smaller than that of CS when the sun position is fixed and the view zenith angle is changed. SCS was mostly 0.12 at different zenith angles, while the CS had a maximum value of 0.45 and a minimum value of 0.15. (2) The accuracy of the simulations was evaluated using the Root Mean Square Error (RMSE). The best RMSE is 0.020 ± 0.015 and the worst one is 0.084 ± 0.044. It was found that the error is larger in short wavelength infrared bands. (3) In this forest, the relative reflectance changed only about 1.2 times as much, as the voxel size was increased from 20 cm to 200 cm. In this study, we have simulated a single Sentinel-2 image. In the future, we will simulate multi-temporal images in order to investigate the effects of phenology and shadow changes on the reflectance that was observed by optical sensors.

Cities lying in the Indo-Gangetic plains of South Asia have the world’s worst anthropogenic air pollution, which is often attributed to urban growth. Brick kilns, facilities for producing fired clay-bricks for construction are often found at peri-urban region of South Asian cities. Although brick kilns are significant air pollutant emitters, their contribution in under-represented in air pollution emission inventories due to unavailability of their distribution. This research overcomes this gap by proposing publicly available remote sensing dataset based approach for mapping brick-kiln locations using object detection and pixel classification. As brick kiln locations are not permanent, an open-dataset based methodology is advantageous for periodically updating their locations. Brick kilns similar to Bull Trench Kilns were identified using the Sentinel-2 imagery around the state of Delhi in India. The unique geometric and spectral features of brick kilns distinguish them from other classes such as built-up, vegetation and fallow-land even in coarse resolution imagery. For object detection, transfer learning was used to overcome the requirement of huge training datasets, while for pixel-classification random forest algorithm was used. The method achieved a recall of 0.72, precision of 0.99 and F1 score of 0.83. Overall 1564 kilns were detected, which are substantially higher than what was reported in an earlier study over the same region. We find that brick kilns are located outside urban areas in proximity to outwardly expanding built-up areas and tall built structures. Duration of brick kiln operation was also estimated by analyzing the time-series of normalized difference vegetation index (NDVI) over the brick kiln locations. The brick kiln locations can be further used for updating land-use emission inventories to assess particulate matter and black carbon emissions.

Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. This study aims to quantify the conversions among mangroves and other land covers with high accuracy. The updated mangrove base map for 2018 was produced by integrating Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) yearly mosaics and Landsat imagery with an overall accuracy of 95.23 ± 6.02%. Then, a novel approach combining map-to-image and image-to-image methods was proposed to detect the changed pixels in mangrove forests from 1985 to 2018. The mangrove base map was adopted to mask the images from other years. To determine the changed pixels, the differencing values in the masked area between two images were calculated and compared with the corresponding thresholds. Based on the changed pixels, the possible driving forces were analyzed and associated with socioeconomic development. The resultant mangrove dynamics demonstrated that mangrove forests in China experienced a tendency of loss first and recovery later during the past 30 years. Most mangrove gains came from aquaculture and mudflat, whilst losses were due to the built-up construction and aquaculture reclamation. These conversions indicated that mangrove deforestations were mainly due to human-induced destruction, while the recoveries were strongly associated with conservation and restoration actions.

Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R2) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R2 = 0.683, RMSE = 25.08 Mg·ha−1) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha−1 to 142 Mg·ha−1 (with an average of 72.47 Mg·ha−1). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.

Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (Re), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch−Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (Re) based on a relationship between the in situ GWT and flux-tower-observed Re. Fire emissions are a large direct source of CO2 from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) Re, GPP, FE, and NBE from whole peatlands in the study area (6°N−11°S, 95−141°E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. Re and FE were influenced by El Niño, and the value of the NBE was also high in the El Niño period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of Re with delineations of types of anthropogenic impacts (e.g., drainages and fires).

Pine wilt disease (PWD) constitutes a serious threat to pine forests. Since development depends on temperature and drought, there is a concern that future climate change could lead to the spread of PWD infections. We evaluated the risk of PWD in 21 susceptible Pinus species on a global scale. The MB index, which represents the sum of the difference between the mean monthly temperature and 15 when the mean monthly temperatures exceeds 15°C, was used to determine current and future regions vulnerable to PWD (MB ≥ 22). For future climate conditions, we compared the difference in PWD risks among four different representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5) and two time periods (2050s and 2070s). We also evaluated the impact of climate change on habitat suitability for each Pinus species using species distribution models. The findings were then integrated and the potential risk of PWD spread under climate change was discussed. Within the natural Pinus distribution area, southern parts of North America, Europe, and Asia were categorized as vulnerable regions (MB ≥ 22; 16% of the total Pinus distribution area). Representative provinces in which PWD has been reported at least once overlapped with the vulnerable regions. All RCP scenarios showed expansion of vulnerable regions in northern parts of Europe, Asia, and North America under future climate conditions. By the 2070s, under RCP 8.5, an estimated increase in the area of vulnerable regions to approximately 50% of the total Pinus distribution area was revealed. In addition, the habitat conditions of a large portion of the Pinus distribution areas in Europe and Asia were deemed unsuitable by the 2070s under RCP 8.5. Approximately 40% of these regions overlapped with regions deemed vulnerable to PWD, suggesting that Pinus forests in these areas are at risk of serious damage due to habitat shifts and spread of PWD.

In Thailand, crop depredation by wild elephants intensified, impacting the quality of life of local communities and long-term conservation of wild elephant populations. Yet, fewer studies explore the landscape-scale spatiotemporal distribution of human−elephant conflict (HEC). In this study, we modeled the potential HEC distribution in ten provinces adjacent to protected areas in Eastern Thailand from 2009 to 2018. We applied the time-calibrated maximum entropy method and modeled the relative probability of HEC in varying scenarios of resource suitability and direct human pressure in wet and dry seasons. The environmental dynamic over the 10-year period was represented by remotely sensed vegetation, meteorological drought, topographical, and human-pressure data. Results were categorized in HEC zones using the proposed two-dimensional conflict matrix. Logistic regression was applied to determine the relevant contribution of each scenario. The results showed that although HEC probability varied across seasons, overall HEC-prone areas expanded in all provinces from 2009 to 2018. The largest HEC areas were estimated during dry seasons with Chantaburi, Chonburi, Nakhon Ratchasima, and Rayong provinces being the HEC hotspots.However, the HEC potential was reduced during severe and prolonged droughts caused by El Nino events. Direct human pressure caused a more gradual increase of HEC probability around protected areas. On the other hand, resource suitability showed large variation across seasons. We recommend zone-dependent management actions towards a fine-balance between human development and the conservation of wild elephants.

The introduction of solar photovoltaic (PV) systems in isolated areas which are far from the main grid has provided energy to non-electrified households. Such off-grid technology is very promising in the Asia Pacific region where increase in population and regional development has brought an increase in energy demand. This paper presents a methodology to assess the available supply of energy from solar PV systems and the corresponding demand from non-electrified areas. Non-electrified high population density areas were extracted using global population distribution and nightlight data, while the suitability of installing solar PV systems in those areas were identified based on slope, land cover and estimated solar PV power output. Moreover, the cost and benefits of installation were estimated based on the levelized cost of electricity generation from PV (LCOEPV) and the percentage in the total household budget that can shoulder the said expense. Lastly, this study also proposed a novel and simple method to extract the power transmission lines (TLs) based on global road network and nightlight data used for defining off-grid areas. Results show that there are three general types of electrification trend in the region with only 11 out 28 countries exhibiting the ideal trend of decreasing population living in unlit areas with increasing GDP. This study also generated maps showing the spatial distribution of high potential areas for solar PV installation in Cambodia, North Korea and Myanmar as case studies. To date, the high estimated household income allotted for PV electricity is still experienced in most countries in the region, but these countries also have high initial generated electricity from PV systems. Outputs from this study can provide stakeholders with relevant information on the suitable areas for installations in the region and the expected socio-economic benefits.

Extreme weather/climate events have been increasing partly due to on-going climate change [...]

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

Vertical urban growth in the form of urban volume or building height is increasingly being seen as a significant indicator and constituent of the urban environment. Although high-resolution digital surface models can provide valuable information, various places lack access to such resources. The objective of this study is to explore the feasibility of using open digital surface models (DSMs), such as the AW3D30, ASTER, and SRTM datasets, for extracting digital building height models (DBHs) and comparing their accuracy. A multidirectional processing and slope-dependent filtering approach for DBH extraction was used. Yangon was chosen as the study location since it represents a rapidly developing Asian city where urban changes can be observed during the acquisition period of the aforementioned open DSM datasets (2001⁻2011). The effect of resolution degradation on the accuracy of the coarse AW3D30 DBH with respect to the high-resolution AW3D5 DBH was also examined. It is concluded that AW3D30 is the most suitable open DSM for DBH generation and for observing buildings taller than 9 m. Furthermore, the AW3D30 DBH, ASTER DBH, and SRTM DBH are suitable for observing vertical changes in urban structures.

The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin⁻destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.

In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas.

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations.

Inferior vena cava filters are effective for preventing the passage of thrombi into the pulmonary arteries in patients with pulmonary embolism and deep vein thrombosis. These filters are indicated in patients with contraindications to anticoagulant therapy or in patients with recurrent acute pulmonary embolism despite the administration of anticoagulant therapy. However, the occurrence of filter-related complications, such as filter migration to the heart, has been increasing. Herein, we report a case of OptEase inferior vena cava filter misplacement in the right atrium. Although the filter migrated to the right ventricle, it was successfully removed and repositioned in the inferior vena cava using endovascular techniques. Unfortunately, moderate tricuspid regurgitation developed, due to the damage to the tricuspid valve that was caused by the procedure. We have also reviewed the relevant literature and discussed the possible strategies for managing cases of filter migration to the heart and preventing filter misplacement.

Urban air pollution is one of the most widespread global sustainability problems. Previous research has studied growth or fall of particulate matter (PM) levels using on-ground monitoring stations in urban regions. However, studying this worldwide is difficult because most cities do not have sufficient infrastructure to monitor air quality. Thus, satellite data is increasingly being employed to solve this limitation. In this paper, we use 16 years (2001–2016) of aerosol optical depth (AOD) and Angstrom exponent ( α ) datasets, retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on the National Aeronautics and Space Administration’s (NASA) Terra satellite to study air quality over 60 locations globally. We propose a novel technique, called AirRGB decomposition, to characterize urban air quality by decomposing AOD and α retrievals into ‘components’ of three distinct scenarios. In the AirRGB decomposition method, using AOD and α dataset three scenarios were investigated: ‘R’—high α and high AOD, ‘G’—high α and low AOD, and ‘B’—low α and low AOD values. These scenarios were mapped and quantified over a triangular red, green and blue color scale. This visualization easily segregates regions having a high concentration of industrial aerosol from only natural aerosols. Our analysis indicates that a sharp divide exists between North American and European cities and Asian cities in terms of baseline pollution and slopes of R and G trends. We found that while pollution in cities in China has started to decrease (e.g., since 2011 for Beijing), it continues to increase in South Asia and Southeast Asia. e.g., R offset of Beijing and New Delhi was 54.98 and 50.43 respectively but R slope was −0.04 and 0.08 respectively. High offset (≥45) and slope (≥0.025) of B for New York, Tokyo, Sydney and Sao Paolo shows that they have clean air, which is still getting better.

Tropical countries like Cambodia require information about forest biomass for successful implementation of climate change mitigation mechanism related to Reducing Emissions from Deforestation and forest Degradation (REDD+). This study investigated the potential of Phased Array-type L-band Synthetic Aperture Radar Fine Beam Dual (PALSAR FBD) 50 m mosaic data to estimate Above Ground Biomass (AGB) in Cambodia. AGB was estimated using a bottom-up approach based on field measured biomass and backscattering (σ(o)) properties of PALSAR data. The relationship between the PALSAR σ(o) HV and HH/HV with field measured biomass was strong with R(2) = 0.67 and 0.56, respectively. PALSAR estimated AGB show good results in deciduous forests because of less saturation as compared to dense evergreen forests. The validation results showed a high coefficient of determination R(2) = 0.61 with RMSE = 21 Mg/ha using values up to 200 Mg/ha biomass. There were some uncertainties because of the uncertainty in the field based measurement and saturation of PALSAR data. AGB map of Cambodian forests could be useful for the implementation of forest management practices for REDD+ assessment and policies implementation at the national level.