A Systematic Review on the Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Health Attributes, Indicators, and Measures

Augmented future: tracing the trajectory of location-based augmented reality gaming for the next ten years

Location-based games are a highly technology-dependent game genre that has witnessed an exponential increase in popularity with the democratisation of smartphones as well as ubiquitous mobile data and access to satellite navigation. Moving forward into the future, location-based games can be expected to evolve as the technologies underlying the genre improve. In this conceptual work, we review the current state of the art in location-based games, and identify key trajectories and trends. We discovered 12 trends, based on which we jump ten years into the future and evaluate how current technology trends may end up influencing location-based gaming. For example, we propose that in the year 2035 through improvements in map data services and sensor data coverage, we will see locative games that are increasingly connected to elements in the physical world. We also expect to see gameplay that moves away from solely taking place on a smartphone screen to the adoption of multiple forms of interactions with location-based game worlds, especially as head-mounted displays and other wearables become more commonplace.

Monitoring vegetation- and geodiversity with remote sensing and traits

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Use of remote sensing-based pressure-state-response framework for the spatial ecosystem health assessment in Langfang, China

Land use/land cover changes are occurring at an unprecedented rate and scale because of the economic development that has jeopardized the ecosystem's health. Ecosystem health should be studied and monitored at spatiotemporal scale to promote sustainable development and ecological civilization. The goal of this study was to assess the spatial ecosystem health of Langfang at the city and administrative levels using city's regional characteristics. Remote sensing-based pressure-state-response (PSR) framework, analytical hierarchy process (AHP), and principal component analysis (PCA) were utilized for spatial ecosystem health index (SEHI) formulation, indicator weighting, and indicator selection in several epochs (1990, 2003, 2013, and 2021), respectively. SEHI was formulated by combining subindices of pressure, state and response. The spatial ecosystem pressure index (SEIP) identified that the pressure was increasing on the ecosystem. In contrast, the spatial ecosystem state index (SEIS) pointed out an improvement in the state of the ecosystem since 1990. The worst state of the ecosystem was observed for the year 2013. The spatial ecosystem response index (SEIR) indicated that the response of the ecosystem towards the exerted pressures and states remained variable; however, it was reasonably good in 1990. All the administrative units of Langfang were associated with a healthy score for the spatial ecosystem health index (SEHI) for 1990 (pre-industrialization epoch), while the SEHI significantly reduced in 2013 (industrialization epoch) however improved for the later epochs (circular economy and ecological civilization epoch). The SEHI was moderately healthy for Dachang, Dacheng, Guan, Guangyang, and Yongqing while relatively healthy for the remaining administrative units in 2021. SEHI identified that spatial health has been improving since 2003 though not reaching the 1990's level for Langfang. Therefore, efforts should be focused on minimizing pressure and stabilizing the state to improve the spatial ecosystem health of Langfang. The developed SEHI can assist policymakers in analyzing regional health, identifying development strategies, driving environmental restoration, and quantifying needed changes.

Mapping the Shifting Focus in Remote Sensing Literature: Technology, Methodology, and Applications

This paper characterizes the body of knowledge on remote sensing from 1999 to 2021 by employing bibliometric techniques based on the Science Citation Index databases and the Social Science Citation Index of the Web of Science, abbreviated to “SCI” and “SSCI”, respectively. A total of 28,438 articles were analyzed from various aspects of the publication characteristics, such as countries, institutes, subjects, journals, and keywords. Dynamic changes in published remote sensing research were examined by segregating the 19-year period into 4 stages. Co-occurrences of keywords from three aspects were evaluated, including technology, methodology, and applications. Results show that “hyperspectral remote sensing”, “classification”, “monitoring” and “MODIS” in the category of technology have emerged more frequently in recent years, and there are strong co-occurrences of “remote sensing” and “GIS” in the remote sensing technology category. In addition, there was a marked shift from traditional analytical methods (i.e., geostatistics and neural networks) to a variety of emerging methods, such as support vector machines, random forests, and feature extraction. Moreover, research hotspots are identified for remote sensing applications, which have expanded significantly with improvements in technology and methodology. In particular, “water quality”, “climate change”, and “urbanization” have become popular themes in recent years. Finally, future directions of remote sensing are identified, which would be beneficial for researchers and policy makers.

Ashes to ashes, and dust to dust: Is scattering garden the sustainable destination for cremated ashes?

Cremation is commonly practiced around the world because it requires small space for the disposal of ashes. Among various options for ash disposal, many people choose to scatter the ashes of their loved ones in a scattering garden. What are the impacts of ash scattering on the vegetation of the garden? Is scattering garden a sustainable solution to the disposal of cremated ashes? This study aimed at answering these questions by characterizing and assessing the vegetation performance of a scattering garden using remote sensing techniques and field measurements. The results indicated that, overall, approximately half of vegetation was degraded to either unhealthy or bare soil. The area of bare soil in the lawns of high scattering level was larger than that of low scattering level. Furthermore, the belowground biomass of vegetation in the lawns of high scattering level was significantly lower than that of low scattering level. It is concluded that the current practice of ash scattering in Hong Kong was not sustainable and the intensity of impacts was dependent upon the level of ash scattering. The findings of this study may provide a reference for the policy and management of ash scattering in Hong Kong and other cities around the world.

Fine Resolution Imagery and LIDAR-Derived Canopy Heights Accurately Classify Land Cover with a Focus on Shrub/Sapling Cover in a Mountainous Landscape

Publicly available land cover maps do not accurately represent shrubs and saplings, an uncommon but ecologically relevant cover type represented by woody vegetation <4 m tall. This omission likely occurs because (1) the resolution is too coarse, (2) poor training data are available, and/or (3) shrub/saplings are difficult to discriminate from spectrally similar classes. We present a framework for classifying land cover, including shrub/saplings, by combining open-source fine-resolution (1 m) spectral and structural data across a large (>6000 km2) mountainous region. We hypothesized that the combination of spectral (imagery) and structural (LIDAR) data would allow for discrimination of shrub/sapling cover from other cover types. Specifically, we created training data using segmented four-band imagery from the National Agricultural Imagery Program (NAIP). In addition to spectral information from imagery, we used topographic information (elevation, slope, and aspect) and a LIDAR-derived canopy height model to classify land cover within a pixel-based random forests framework. To assess model accuracy, we used image interpretation and an independent sample of validation points. Due to the fine resolution of predictor rasters across such a large geographic region, we classified five subregions (counties) separately. We also compared the landscape metrics calculated for our custom classification at fine (1 m) and coarse resolution (resampled to 30 m) to metrics calculated with National Land Cover Data (NLCD). We achieved an overall accuracy of 89% and >80% accuracy for each land cover class. The LIDAR-derived canopy height model was consistently ranked as the most important predictor of vegetative land cover classes. Compared with our custom classification, NLCD underrepresented pasture/grassland by up to 10% and overrepresented forest up to 30%. There was no correlation between percent shrub/sapling cover in our custom classification and NLCD, suggesting that NLCD is not reliable for applications concerned with this ecologically relevant cover type.