ALBERT

Description: Forests, Vol. 9, Pages 594: Screening Potential Bioenergy Production of Tree Species in Degraded and Marginal Land in the Tropics Forests doi: 10.3390/f9100594 Authors: Nils Borchard Medha Bulusu Ann-Michelle Hartwig Matthias Ulrich Soo Min Lee Himlal Baral Bioenergy can produce at least 25% of the global energy demand to combat climate change through reducing emissions in the energy sector. However, information on the bioenergy production potential of woody species and their suitability for silviculture on various soils in the humid tropics is limited. This review aims to identify tree species suitable for bioenergy production under these conditions. Data were compiled from 241 publications and nine freely available databases to assess environmental and silvicultural information on tropical tree species. Energy outputs were derived from the estimated productivity of the reviewed species and ranged from 0.2 to 24.0 Mg biomass ha−1 yr−1, 0.1 to 9.0 Mg bio-oil ha−1 yr−1, and 0.2 to 20.0 Mg sugar ha−1 yr−1, equivalent to an energy yield between 2 and 444 GJ ha−1 yr−1. As such, these bioenergy yields are within the range reported for the lignocellulosic biomass of energy crops cultivated in Europe, the USA, and Brazil. Our review identified some high-yielding species (e.g., Dyera polyphylla (Miq.) Steenis, Metroxylon sagu (Rottb.), Pongamia pinnata (L.)) and leguminous species that could be beneficial in mixed stands (e.g., Elaeis oleifera (Kunth) and Pongamia pinnata) or are suitable species to grow on wet or re-wetted peatland (Dyera polyphylla). However, there are limitations to cultivate woody bioenergy species on wet peatland. Sustainable methods for managing and harvesting forests, particularly on wet or re-wetted peatland, need to be developed.

Description: IJGI, Vol. 7, Pages 382: Incremental Road Network Generation Based on Vehicle Trajectories ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100382 Authors: Zhongyi Ni Lijun Xie Tian Xie Binhua Shi Yao Zheng Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

Description: IJGI, Vol. 7, Pages 383: Care, Indifference and Anxiety—Attitudes toward Location Data in Everyday Life ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100383 Authors: Michal Rzeszewski Piotr Luczys Modern mobile devices are replete with advanced sensors that expand the array of possible methods of locating users. This can be used as a tool to gather and use spatial information, but it also brings with it the specter of “geosurveillance” in which the “location” becomes a product in itself. In the realm of software developers, space/place has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often marked with anxiety. We explored attitudes toward location data collection practices using qualitative questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude that we identified is neutral with a strong undertone of resignation—surrendering personal location is viewed as a form of digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be a result of different values associated with space and place itself. Those attitudes can bring additional bias into spatial datasets that is not related to demographics.

Description: Remote Sensing, Vol. 10, Pages 1529: Tomographic Imaging of Ionospheric Plasma Bubbles Based on GNSS and Radio Occultation Measurements Remote Sensing doi: 10.3390/rs10101529 Authors: Fabricio dos Santos Prol Manuel Hernández-Pajares Marcio Tadeu de Assis Honorato Muella Paulo de Oliveira Camargo Total electron content measurements given by the global navigation satellite system (GNSS) have successfully presented results to capture the signatures of equatorial plasma bubbles. In contrast, the correct reproduction of plasma depletions at electron density level is still a relevant challenge for ionospheric tomographic imaging. In this regard, this work shows the first results of a new tomographic reconstruction technique based on GNSS and radio-occultation data to map the vertical and horizontal distributions of ionospheric plasma bubbles in one of the most challenging conditions of the equatorial region. Twenty-three days from 2013 and 2014 with clear evidence of plasma bubble structures propagating through the Brazilian region were analyzed and compared with simultaneous observations of all-sky images in the 630.0 nm emission line of the atomic oxygen. The mean rate of success of the tomographic method was 37.1%, being more efficient near the magnetic equator, where the dimensions of the structures are larger. Despite some shortcomings of the reconstruction technique, mainly associated with ionospheric scintillations and the weak geometry of the ground-based GNSS receivers, both vertical and horizontal distributions were mapped over more than 30° in latitude, and have been detected in instances where the meteorological conditions disrupted the possibility of analyzing the OI 630 nm emissions. Therefore, the results revealed the proposed tomographic reconstruction as an efficient tool for mapping characteristics of the plasma bubble structures, which may have a special interest in Space Weather, Spatial Geodesy, and Telecommunications.

Description: Remote Sensing, Vol. 10, Pages 1528: Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation Remote Sensing doi: 10.3390/rs10101528 Authors: Liang Han Guijun Yang Haikuan Feng Chengquan Zhou Hao Yang Bo Xu Zhenhai Li Xiaodong Yang Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

Description: Remote Sensing, Vol. 10, Pages 1527: Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia Remote Sensing doi: 10.3390/rs10101527 Authors: Dieu Tien Bui Himan Shahabi Ataollah Shirzadi Kamran Chapi Mohsen Alizadeh Wei Chen Ayub Mohammadi Baharin Bin Ahmad Mahdi Panahi Haoyuan Hong Yingying Tian Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

Description: Remote Sensing, Vol. 10, Pages 1526: NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies Remote Sensing doi: 10.3390/rs10101526 Authors: Xizhi Zhao Bailang Yu Yan Liu Shenjun Yao Ting Lian Liujia Chen Chengshu Yang Zuoqi Chen Jianping Wu Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.

Description: Remote Sensing, Vol. 10, Pages 1525: Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015 Remote Sensing doi: 10.3390/rs10101525 Authors: Lanhui Li Yili Zhang Linshan Liu Jianshuang Wu Zhaofeng Wang Shicheng Li Huamin Zhang Jiaxing Zu Mingjun Ding Basanta Paudel Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.

Description: Forests, Vol. 9, Pages 592: Strategies for Climate-Smart Forest Management in Austria Forests doi: 10.3390/f9100592 Authors: Robert Jandl Thomas Ledermann Georg Kindermann Alexandra Freudenschuss Thomas Gschwantner Peter Weiss We simulated Austrian forests under different sustainable management scenarios. A reference scenario was compared to scenarios focusing on the provision of bioenergy, enhancing the delivery of wood products, and reduced harvesting rates. The standing stock of the stem biomass, carbon in stems, and the soil carbon pool were calculated for the period 2010–2100. We used the forest growth model Câldis and the soil carbon model Yasso07. The wood demand of all scenarios could be satisfied within the simulation period. The reference scenario led to a small decrease of the stem biomass. Scenarios aiming at a supply of more timber decreased the standing stock to a greater extent. Emphasizing the production of bioenergy was successful for several decades but ultimately exhausted the available resources for fuel wood. Lower harvesting rates reduced the standing stock of coniferous and increased the standing stock of deciduous forests. The soil carbon pool was marginally changed by different management strategies. We conclude that the production of long-living wood products is the preferred implementation of climate-smart forestry. The accumulation of carbon in the standing biomass is risky in the case of disturbances. The production of bioenergy is suitable as a byproduct of high value forest products.

Description: Forests, Vol. 9, Pages 593: Macronutrient Stocks in Scots Pine Stands of Different Densities Forests doi: 10.3390/f9100593 Authors: Andrzej Węgiel Ernest Bielinis Krzysztof Polowy A positive nutrient balance is crucial to sustaining forest productivity. Differences in stand densities usually mean different aboveground biomass stocks and different proportions of tree compartments. These differences can be reflected in the different macronutrient stocks between stands of different densities, because various tree compartments have different element concentrations. In this study, 82-year-old stands of Scots pine were compared, and specifically, the concentrations of the elements in tree compartments and the amounts of macronutrients in aboveground biomass were compared. The nutrients considered in this study were nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur. A positive correlation between stand density and the level of stored macronutrients was found for nitrogen, phosphorus, and potassium. This result means that forest managers can influence nutrient balances by regulating stand densities or by harvesting methods (SOH: stem-only harvesting or WTH: whole-tree harvesting).