ALBERT

Description: Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs’ emphasis to make geospatial data available through a participatory approach.

Description: East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

Description: Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap.

Description: A low-mass and low-volume dual-polarization L-band radiometer is introduced that has applications for ground-based remote sensing or unmanned aerial vehicle (UAV)-based mapping. With prominent use aboard the ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, L-band radiometry can be used to retrieve environmental parameters, including soil moisture, sea surface salinity, snow liquid water content, snow density, vegetation optical depth, etc. The design and testing of the air-gapped patch array antenna is introduced and is shown to provide a 3-dB full power beamwidth of 37°. We present the radio-frequency (RF) front end design, which uses direct detection architecture and a square-law power detector. Calibration is performed using two internal references, including a matched resistive source (RS) at ambient temperature and an active cold source (ACS). The radio-frequency (RF) front end does not require temperature stabilization, due to characterization of the ACS noise temperature by sky measurements. The ACS characterization procedure is presented. The noise equivalent delta (Δ) temperature (NEΔT) of the radiometer is ~0.14 K at 1 s integration time. The total antenna temperature uncertainty ranges from 0.6 to 1.5 K.

Description: Avalanche disasters are extremely destructive and catastrophic, often causing serious casualties, economic losses and surface erosion. However, far too little attention has been paid to utilizing remote sensing mapping avalanches quickly and automatically to mitigate calamity. Such endeavors are limited by formidable natural conditions, human subjective judgement and insufficient understanding of avalanches, so they have been incomplete and inaccurate. This paper presents an objective and widely serviceable method for regional auto-detection using the scattering and interference characteristics of avalanches extracted from Sentinel-1 SLC images. Six indices are established to distinguish avalanches from surrounding undisturbed snow. The active avalanche belts in Kizilkeya and Aktep of the Western TianShan Mountains in China lend urgency to this research. Implementation found that smaller avalanches can be consistently identified more accurately in descending images. Specifically, 281 and 311 avalanches were detected in the ascending and descending of Kizilkeya, respectively. The corresponding numbers on Aktep are 104 and 114, respectively. The resolution area of single avalanche detection can reach 0.09 km2. The performance of the model was excellent in all cases (areas under the curve are 0.831 and 0.940 in descending and ascending of Kizilkeya, respectively; and 0.807 and 0.938 of Aktep, respectively). Overall, the evaluation of statistical indices are POD 〉 0.75, FAR 〈 0.34, FOM 〈 0.13 and TSS 〉 0.75. The results indicate that the performance of the innovation proposed in this paper, which employs multivariate comprehensive descriptions of avalanche characteristics to actualize regional automatic detection, can be more objective, accurate, applicable and robust to a certain extent. The latest and more complete avalanche inventory generated by this design can effectively assist in addressing the increasingly severe avalanche disasters and improving public awareness of avalanches in alpine areas.

Description: An extremely heavy rainfall event hit Guangdong province, China, from 27 August to 1 September 2018. There were two different extreme rain regions, respectively, at the Pearl River estuary and eastern Guangdong, and a record-breaking daily precipitation of 1056.7 mm was observed at Gaotan station on 30 August. This paper utilizes a suite of observations from soundings, a gauge network, disdrometers, and polarimetric radars to gain insights to the two rainfall centers. The large-scale meteorological forcing, rainfall patterns, and microphysical processes, as well as radar-based precipitation signatures are investigated. It is concluded that a west-moving monsoon depression played a critical role in sustaining the moisture supply to the two extreme rain regions, and the combined orographic enhancement further contributed to the torrential rainfall over Gaotan station. The raindrop size distributions (DSD) observed at Zhuhai and Huidong stations, as well as the observed polarimetric radar signatures indicate that the rainfall at Doumen region was characterized by larger raindrops but a lower number concentration compared with that at Gaotan region. In addition, the dual-polarization radars are used to quantify precipitation intensity during this extreme event, providing timely information for flood warning and emergency management decision-making.

Description: Earth remote sensing optical satellite systems are often divided into two categories—geosynchronous and sun-synchronous. Geosynchronous systems essentially rotate with the Earth and continuously observe the same region of the Earth. Sun-synchronous systems are generally in a polar orbit and view differing regions of the Earth at the same local time. Although similar in instrument design, there are enough differences in these two types of missions that often the calibration of the instruments can be substantially different. Thus, respective calibration teams develop independent methods and do not interact regularly or often. Yet, there are numerous areas of overlap and much to learn from one another. To address this issue, a panel of experts from both types of systems was convened to discover common areas of concern, areas where improvements can be made, and recommendations for the future. As a result of the panelist’s efforts, a set of eight recommendations were developed. Those that are related to improvements of current technologies include maintaining sun-synchronous orbits (not allowing orbital decay), standardization of spectral bandpasses, and expanded use of well-developed calibration techniques such as deep convective clouds, pseudo invariant calibration sites, and lunar methodologies. New techniques for expanded calibration capability include using geosynchronous instruments as transfer radiometers, continued development of ground-based prelaunch calibration technologies, expansion of RadCalNet, and development of space-based calibration radiometer systems.

Description: As satellite observation technology improves, the number of remote sensing images significantly and rapidly increases. Therefore, a growing number of studies are focusing on remote sensing image retrieval. However, having a large number of remote sensing images considerably slows the retrieval time and takes up a great deal of memory space. The hash method is being increasingly used for rapid image retrieval because of its remarkably fast performance. At the same time, selecting samples that contain more information and greater stability to train the network has gradually become the key to improving retrieval performance. Given the above considerations, we propose a deep hash remote sensing image retrieval method, called the hard probability sampling hash retrieval method (HPSH), which combines hash code learning with hard probability sampling in a deep network. Specifically, we used a probability sampling method to select training samples, and we designed one novel hash loss function to better train the network parameters and reduce the hashing accuracy loss due to quantization. Our experimental results demonstrate that HPSH could yield an excellent representation compared with other state-of-the-art hash approaches. For the university of California, merced (UCMD) dataset, HPSH+S resulted in a mean average precision (mAP) of up to 90.9% on 16 hash bits, 92.2% on 24 hash bits, and 92.8% on 32 hash bits. For the aerial image dataset (AID), HPSH+S achieved a mAP of up to 89.8% on 16 hash bits, 93.6% on 24 hash bits, and 95.5% on 32 hash bits. For the UCMD dataset, with the use of data augmentation, our proposed approach achieved a mAP of up to 99.6% on 32 hash bits and 99.7% on 64 hash bits.

Description: Access and use of seed of improved varieties of groundnut among farmers can improve farmers’ livelihoods and contribute to the potential of crop production in Tanzania. This paper analyzes factors underpinning the adoption of improved groundnut varieties among farmers to pave the way for upscaling quality seed used for increased production and commodity business in farming communities. A four-stage stratified sampling was used to collect data from 300 groundnut farmers in seven agro-ecological zones through individual interviews. Secondary data were collected from the literature and the Tanzania Agricultural Research Institute at Naliendele centre (TARI–Naliendele). Descriptive statistics and Probit regression model were used for data analysis. The empirical results showed that Johari 1985, Pendo 1998, Naliendele 2009, Mnanje 2009, Mangaka 2009 and Nachi 2015, are the main six improved groundnut varieties used by farmers, with Pendo 1998 having the highest adoption rate (17.1%). In the grain market, four varieties, namely Pendo 1998, Mnanje 2009, Nachi 2015 and Johari 1985, were observed to be highly preferred by grain off-takers. Furthermore, among the adopted improved varieties, Nachi 2015, is observed to be the most consistent high yielding variety, ranging from 1100 kg/ha to 1500 kg/ha in all agro-ecological zones. A farmer’s decision to adopt new varieties is affected by age and gender, farmer group membership, availability of improved seed and seed cost. Overall, male farmers are more likely to adopt improved varieties of groundnut than female farmers. The implications of these findings are also discussed, in particular in the area of policy support.

Description: Green bonds are similar to conventional bonds but are specifically earmarked to raise money to finance climate or environmental projects. There have been anecdotes of green bonds being priced tighter than similar conventional bonds by the same issuers. Our survey of academic literature indicates that most papers show the yield of a green bond is lower than that of the equivalent conventional bond at issuance (also known as green premium or greenium). However, green bond pricing studies by Climate Bonds Initiative produce mixed results. The conflicting results are likely explained by differences in sample selections, time periods, methodologies, and the properties of the respective issuing entity and the bond. In addition, we examine investment returns from select green bond funds and green bond indexes. The assets under management of those funds are still small and they underperform their benchmark indexes.