High Spatial Resolution Data Research Articles

Identifying, projecting, and evaluating informal urban expansion spatial patterns

ABSTRACT Informal urban land expansion is produced through a diversity of social and political transactions, yet ‘pixelizable’ data capturing these transactions is commonly unavailable. Understanding informal urbanization entails differentiating spatial patterns of informal settlement from formal growth, associating such patterns with the social transactions that produce them, and evaluating the social and environmental outcomes of distinct settlement types. Demonstrating causality between distinct urban spatial patterns and social-institutional processes requires both high-resolution spatial temporal time-series data of urban change and insights into social transactions giving rise to these patterns. We demonstrate an example of linking distinct spatial patterns of informal urban expansion to the institutional processes each engenders in Mexico City. The approach presented here can be applied across cases, potentially improving land projection models in the rapidly urbanizing Global South, characterized by high informality. We conclude with a research agenda to identify, project, and evaluate informal urban expansion patterns.

Advancement of fluid measurement using mode decomposition

In this presentation, recent advanced fluid measurement based on active use of modal decomposition will be introduced. One is sparse processing particle image velocimetry(SPPIV) for real time estimation of flow fields. This technique uses proper orthogonal decomposition (POD), optimized processing point selection in the particle image velocimetry(PIV), and a Kalman filter, and achieves 2000 Hz real time flow observation of 10 m/s flow around airfoil. The other is spatio-temporal superresolution of supersonic flow. This technique combines the microphone data of high temporal but low spatial resolution and the PIV data of low temporal but high spatial resolution by reduced-order model, and the flow data of high temporal and spatial resolution is estimated. Those techniques pioneer the new frontier of the advanced fluid measurements.

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luft - und Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400-1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have 4 multispectral broadbands (MBB) with 3-4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72-100% and 75-100% respectively for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56-100% and 61-100% respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65-100% and 60-94% respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

PRISMA Spatial Resolution Enhancement by Fusion With Sentinel-2 Data

This article deals with the problem of improving the spatial resolution of hyperspectral (HS) data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, 10 S2 bands at 10 and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The article presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low-resolution PRISMA image, provides a super-resolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA HS sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to coregister the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved HS image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR.

Опыт разработки локальной ГИС орошаемых земель Волго-Ахтубинской поймы на территории Волгоградской области

The Volga-Akhtuba floodplain is a particularly valuable natural object in the south of Russia, located on the territory of three constituent entities of our country. There are three regional and one federal protected areas. Trends in the agricultural use of floodplain lands are aimed at increasing acreage and intensifying agriculture. Under these conditions, the risks of various pollution of soil and water resources increase. Land use control, as well as sown areas, is impossible without knowledge of the current state and location of cultivated irrigated lands. The modern policy of the state is aimed at involving unused agricultural land, one of the stages of which is updating information. The paper presents the results of the development of a local geoinformation system for irrigated lands of the Volga-Akhtuba floodplain within the Volgograd region. The purpose of the work is to create and maintain the functioning of a local geoinformation system of irrigated fields, natural and anthropogenic objects that form the general attractiveness of sites in terms of resuming economic activity, in the context of rural settlements located within the Volga-Akhtuba floodplain on the territory of the Volgograd areas. The author has developed thematic geoinformation layers in three blocks: natural, economic and analytical. The main materials for compiling the thematic layers were Earth remote sensing data of high and ultra-high spatial resolution. Substantiated are various methods for the formation of geoinformation layers, as well as the problems of identifying irrigated lands by automated methods and the prospects for automating the process of determining the use of irrigated lands. The general structure of a local GIS consists of more than 20 layers and 50 attribute datasets. A local geoinformation system can be presented as a web GIS and used in landscape and environmental studies, as well as to optimize measures to involve agricultural land.

Research on Spatial Statistical Downscaling Method of Meteorological Data Applied to Photovoltaic Prediction

Aiming at the low spatial resolution of meteorological data output from a numerical model in photovoltaic power prediction, a geographically weighted statistical downscaling method considers the influence factors such as normalized vegetation index (NDVI), digital elevation model (DEM), slope direction, longitude and latitude is proposed. This method is based on the correlation between meteorological data and NDVI, DEM, slope direction, latitude and longitude, and introduces DEM and local Moran index to improve the regression model, and obtains 100 * 100 m high-resolution meteorological spatial distribution data. Finally, combining the measured data of the study area and the established EOF iterative downscaling method to verify and compare the downscaling results. The results show that the error between the downscaled meteorological data and the measured value is smaller, and the comprehensive downscaling accuracy of the geographically weighted regression method is higher, and the model fitting effect is better. Therefore, this method can effectively improve the influence of errors caused by lower resolution, and provide a more reliable meteorological basis for the prediction of photovoltaic power.

Spatial downscaling method and application of CMIP5 global climate models based on improved U-Net in Lake Poyang Basin

利用降尺度方法对CMIP5全球气候模式进行空间降尺度并以此研究鄱阳湖流域未来气候时空变化趋势，能够为流域生态环境保护提供数据、技术和理论上的支持.通过简化原始网络结构，在网络首部添加插值层，采用反卷积算法作为上采样算法对传统U-Net网络进行改进，建立基于深度学习的气候模式空间降尺度模型（DLDM）.以1965-2005年鄱阳湖流域共18个气象站点的实测数据为基准，基于IPSL-CM5A-LR和BCC-CSM1.1两个气候模式，在拟合精度和流域极端气候事件模拟能力两方面对比验证了降尺度方法和降尺度后气候模式的模拟性能，结果表明基于DLDM的方法优于基于线性回归的传统统计降尺度方法，IPSL-CM5A-LR模式模拟效果优于BCC-CSM1.1模式.利用DLDM对RCP2.6和RCP8.5两个排放情景下的IPSL-CM5A-LR模式数据进行空间降尺度，基于2006-2100年流域高空间分辨率气候数据分析两情景下流域未来气候的时空变化特征，结果表明流域未来气温在两情景下均持续升高，表现出位于流域中北部、西部、东部和南部4个高温中心，RCP8.5情景下流域气温更高，升温趋势和局部周期变化更加明显；流域未来降水在两情景下先增加后减少，变化趋势较为平缓，表现出北部、中东部和南部3个降水中心，在未来呈现五次枯-丰交替，RCP8.5情景下流域降水更少且变化更为剧烈，在2075年左右出现突变并存在周期性振荡.;Predicting spatiotemporal trend of the future climate in Lake Poyang Basin using CMIP5 global climate models downscaled by downscaling methods can provide data, technology, and theoretical support for the ecological and environmental protection of the basin. In current research area downscaling methods mainly include dynamic downscaling method and statistical downscaling method, among which statistical downscaling method based on linear regression is widely used thus defective in modeling non-linear relationships. In order to build up a better downscaling model, the deep learning ased spatial downscaling method (DLDM) which is more efficient in learning climate data features is established by simplifing the original network structure of U-Net, adding an interpolation layer at the head of the network, and using the deconvolution algorithm as upsampling algorithm. Then based on climate models as IPSL-CM5A-LR and BCC-CSM1.1, simulating ability of downscaled climate model is verified by making contrastive analysis to observed data from 18 meteorological stations in Lake Poyang Basin from 1965 to 2005 on fitting precision and ability to simulate extreme weather events. The results show that DLDM has higher fitting accuracy over the linear regression based statistical downscaling method and IPSL-CM5A-LR has higher fitting accuracy over BCC-CSM1.1. Based on high spatial resolution data of IPSL-CM5A-LR downscaled by DLDM from 2006 to 2100, the spatiotemporal trend of future climate in Lake Poyang Basin under RCP2.6 and RCP8.5 emissions scenarios is studied through M-K analysis and wavelet transform. The results show that the future temperature of the basin will continue to increase under the two scenarios, showing four high temperature centers located in the central north, west, east, and south of the basin. Under the RCP8.5 scenario, the temperature of the basin will be higher, and the warming trend and local cycle change will be more obvious. The future precipitation in the basin will increase first and then decrease under the two scenarios, and the changing trend is relatively gentle, showing three precipitation centers in the north, the central east, and the south of the basin. In the future, there will be drought-wet alternation for five times. Under the RCP8.5 scenario, the precipitation in the basin will be less and the change will be more intense, and there will be a sudden change and periodic oscillation at around 2075.

Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data

Net primary productivity (NPP) is an important indicator of Earth’s ecosystem. However, NPP estimation of cities by high spatial resolution is limited in China, especially Nanjing, and NPP evolution and quantitative analysis of influencing factors in time series are insufficient. Nanjing, as one of the central cities in China, plays a major role in China’s economic development. In this paper, high spatial resolution data combined with Landsat Thematic Mapper (TM) data are used for NPP estimation and analysis in Nanjing City. The research contents of this paper are as follows: (1) To improve the passive effect of “red border region” on GF-1 WFV forest and farmland classification ability, the multispectral and high-resolution images are fused, and the image fusion effect was also evaluated. (2) The NPP of four seasons in Nanjing in 2017 was estimated using the CASA model. (3) The results of NPP estimation are analyzed based on statistical principles. The advantage of this paper lies in the comprehensive utilization of multi-source data and the quantitative analysis of the response mechanism between meteorological data and the NPP estimation value.

Spatiotemporal Downscaling Method of Land Surface Temperature Based on Daily Change Model of Temperature

Land surface temperature (LST) is one of the most crucial variables of surface energy processes. However, the trade-off between spatial and temporal resolutions of remote sensing data has greatly limited the availability of concurrently high- spatiotemporal resolution LST data for wide applications. Existing downscaling methods are easily affected by null values of LST data and effective time distribution of high-resolution LST data, resulting in large downscaling errors at sometimes. Within this context, this study proposes a novel spatiotemporal fusion model of LST based on diurnal variation information (BDSTFM) to predict LST data with a high temporal resolution and spatio-temporal continuity based on FY-4A and MODIS. Results indicated that the accuracy of the downscaling results was comparable to that of MODIS LST products. The BDSTFM model exhibited the following characteristics: use low-spatial resolution data to establish a DTC model for scale deduction, and retention of the temporal distribution characteristics of LST data; extend the observation time of high-spatial resolution data to improve the accuracy and stability of the model; add an invalid pixel reconstruction step that considers the LST spatiotemporal continuity, and can obtain a realistic and reliable 1-km seamless LST datasets at hourly intervals under clear skies. Compared to the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), 4-parameter DTC model and random forest model, the BDSTFM model attained a higher downscaling accuracy.

Exploring RPAS potentiality using a RGB camera to understand short term variation on sandy beaches

Remotely Piloted Aircraft Systems (RPAS) is a low-cost and useful alternative to track spatial-temporal variations in a rapidly changing environment. However, there is a lack of studies on beach geomorphology characterization in association with visible spectrum reflectance potentialities. The objective of this study was to embrace this technology for coastal change detection. For this purpose, the data set samples are covering the winter season of 2019. The materials used were digital orthophotos, digital terrain models, and geomorphological parameters, such as the Relative Tide Range (RTR) and Omega (Ω). The goals are to (i) classify the beach morphodynamics; (ii) identify the berms, cusps, and rip current channels; (iii) extract RGB indexes to detect the shoreline variability; (iv) assess beach profiles and (v) evaluate the volume of sediments. The study area, Paiva beach, is located in the Northeast of Brazil, which presents significant morphodynamic variations. The results indicate three morphodynamic stages, namely: Rhythmic Bar and Beach (RBB); Transverse Bar and Rip (TBR), and Low Tide Terrace (LTT). The wave climate for the region averaged Dir of 120°, Tp of 9 s, and Hs of 1.8 m. Between June and July, the shoreline had the most significant advance with 51% of the beach extension. On the other hand, between July and August, the beach registered a considerable 88% retreat. The profiles showed a maximum variation of 1.5 m vertically on the berm, a common characteristic of winter profiles, detected in August. For the sediment volume, July and August showed erosion processes with sediment loss of 12,508.58 m3 in the south sector and accretion of 23,052.97 m3 in the north. The planimetric and altimetric mean square error were 2.7 and 4.5 cm, respectively, showing the advantage of high spatial resolution data for morphodynamic classification, and combining it to identify vegetation indexes to detect shoreline variability.

A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol.

Air pollution is one of the major environmental challenges cities worldwide face today. Planning healthy environments for all future populations, whilst considering the ongoing demand for urbanisation and provisions needed to combat climate change, remains a difficult task. To combine artificial intelligence (AI), atmospheric and social sciences to provide urban planning solutions that optimise local air quality by applying novel methods and taking into consideration population structures and traffic flows. We will use high-resolution spatial data and linked electronic population cohort for Helsinki Metropolitan Area (Finland) to model (a) population dynamics and urban inequality related to air pollution; (b) detailed aerosol dynamics, aerosol and gas-phase chemistry together with detailed flow characteristics; (c) high-resolution traffic flow addressing dynamical changes at the city environment, such as accidents, construction work and unexpected congestion. Finally, we will fuse the information resulting from these models into an optimal city planning model balancing air quality, comfort, accessibility and travelling efficiency.

Territorial impact of the COVID-19 pandemic in Galicia (Spain): a geographical approach

The coronavirus pandemic is causing a huge impact around the world. Its real magnitude presents very important regional differences, which are appreciable in the number of infected and victims in the different countries. The outbreak of the pandemic and the ignorance of the virus mean that, even today, there are many unknowns about essential aspects related to it. In this sense, geographic knowledge can help answer many questions from the territorial analysis of the data. The objective of this article will be to analyze the behavior of the coronavirus pandemic within the Spanish region of Galicia. The authors of this study propose a multiscale analysis that allows deciphering the most common propagation patterns. For this, we have high spatial resolution data that has been provided by the competent authority under confidentiality. The results of this work allow us to represent and interpret the territorial impact of the pandemic, understanding its behavior as far as possible, allowing future dynamics to be predicted.

Micro-climate to macro-risk: mapping fine scale differences in mosquito-borne disease risk using remote sensing

Mosquito-borne diseases (MBD) threaten over 80% of the world’s population, and are increasing in intensity and shifting in geographical range with land use and climate change. Mitigation hinges on understanding disease-specific risk profiles, but current risk maps are severely limited in spatial resolution. One important determinant of MBD risk is temperature, and though the relationships between temperature and risk have been extensively studied, maps are often created using sparse data that fail to capture microclimatic conditions. Here, we leverage high resolution land surface temperature (LST) measurements, in conjunction with established relationships between air temperature and MBD risk factors like mosquito biting rate and transmission probability, to produce fine resolution (70 m) maps of MBD risk components. We focus our case study on West Nile virus (WNV) in the San Joaquin Valley of California, where temperatures vary widely across the day and the diverse agricultural/urban landscape. We first use field measurements to establish a relationship between LST and air temperature, and apply it to Ecosystem Spaceborne Thermal Radiometer Experiment data (2018–2020) in peak WNV transmission months (June–September). We then use the previously derived equations to estimate spatially explicit mosquito biting and WNV transmission rates. We use these maps to uncover significant differences in risk across land cover types, and identify the times of day which contribute to high risk for different land covers. Additionally, we evaluate the value of high resolution spatial and temporal data in avoiding biased risk estimates due to Jensen’s inequality, and find that using aggregate data leads to significant biases of up to 40.5% in the possible range of risk values. Through this analysis, we show that the synergy between novel remote sensing technology and fundamental principles of disease ecology can unlock new insights into the spatio-temporal dynamics of MBDs.

Mapping home internet activity during COVID-19 lockdown to identify occupation related inequalities

During the COVID-19 pandemic, evidence has accumulated that movement restrictions enacted to combat virus spread produce disparate consequences along socioeconomic lines. We investigate the hypothesis that people engaged in financially secure employment are better able to adhere to mobility restrictions, due to occupational factors that link the capacity for flexible work arrangements to income security. We use high-resolution spatial data on household internet traffic as a surrogate for adaptation to home-based work, together with the geographical clustering of occupation types, to investigate the relationship between occupational factors and increased internet traffic during work hours under lockdown in two Australian cities. By testing our hypothesis based on the observed trends, and exploring demographic factors associated with divergences from our hypothesis, we are left with a picture of unequal impact dominated by two major influences: the types of occupations in which people are engaged, and the composition of households and families. During lockdown, increased internet traffic was correlated with income security and, when school activity was conducted remotely, to the proportion of families with children. Our findings suggest that response planning and provision of social and economic support for residents within lockdown areas should explicitly account for income security and household structure. Overall, the results we present contribute to the emerging picture of the impacts of COVID-19 on human behaviour, and will help policy makers to understand the balance between public health and social impact in making decisions about mitigation policies.

Evaluation of the operational Chlorophyll-a product from global ocean colour sensors in the coastal waters, south-eastern Arabian Sea

The present study aims to address the effect of spatial resolution and retrieval algorithms on the performance of operational chlorophyll-a (chl-a) product from different sensors. To meet the objective, chl-a product at different spatial resolutions derived from MODIS, SNPP-VIIRS, OC-CCI, and Sentinel-3A OLCI were compared with in-situ measurements of the coastal waters of Karnataka, south-eastern Arabian Sea and errors were quantified using statistical criteria. MODIS-derived chl-a concentrations at 4 km tends to overestimate in the nearshore waters as shown by a high mean relative percentage difference (MRPD) of 77.09%, and a low coefficient of determination (R2) of 0.35. A significant improvement was seen for MODIS 1 km chl-a product, (R2 = 0.64, MRPD = 22.30%) and Mean Absolute Percentage Deviation (MAPD) = 47.08%. Validation of SNPP-VIIRS derived chl-a of 4 km with in-situ measurements showed moderate correlation (R2 = 0.54, MAPD = 49.21%), whereas 1 km SNPP-VIIRS showed substantially better result than 4 km with majority of data points aggregated along the 1:1 line (R2 = 0.86, MAPD = 43.66%, MRPD = 20.97%). Chlorophyll-a product at original resolution (750 m) showed less error than 4 km and 1 km resolution (MAPD = 39.18%, R2 = 0.75, MRPD = 10.875%). In contrast, OC-CCI underestimated field measured chl-a concentrations as indicated by MRPD (-36.72%), with moderate R2 = 0.58. All 4 km data showed poor results than higher spatial resolution data. Sentinel-3A OLCI 300 m full-resolution (FR) derived chl-a, showed good agreement with sea-truth data (R2 = 0.80, MAPD = 33.07%, and MRPD = 23.89%), whereas Sentinel-3A reduced-resolution (RR-1.2 km) exhibited slight overestimation (MRPD = 28.43%, MAPD = 39.88%, R2 = 0.78) than Sentinel-3A(FR). Comparison of operational chl-a product from OC-CCI, MODIS, SNPP-VIIRS, and Sentinel-3A OLCI illustrated that Sentinel-3A OLCI performed best in coastal waters of Karnataka indicating the importance of higher spatial resolution and neural network-based algorithms.

Forest classification using synthetic GF-1/WFV time series and phenological parameters

High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ≥ 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management.

Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services

Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry.

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE.

Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high-throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. Here, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI data sets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus, facilitating applications of MSI in glycobiology.

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.