Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.
Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment.
Natural infrastructure such as parks, forests, street trees, green roofs, and coastal vegetation is central to sustainable urban management. Despite recent progress, it remains challenging for urban decision-makers to incorporate the benefits of natural infrastructure into urban design and planning. Here, we present an approach to support the greening of cities by quantifying and mapping the diverse benefits of natural infrastructure for now and in the future. The approach relies on open-source tools, within the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) software, that compute biophysical and socio-economic metrics relevant to a variety of decisions in data-rich or data-scarce contexts. Through three case studies in China, France, and the United States, we show how spatially explicit information about the benefits of nature enhances urban management by improving economic valuation, prioritizing land use change, and promoting inclusive planning and stakeholder dialogue. We discuss limitations of the tools, including modeling uncertainties and a limited suite of output metrics, and propose research directions to mainstream natural infrastructure information in integrated urban management.
Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.
Landsat 8, the ongoing mission of the Landsat satellites that have provided over 40 years of images, continues to benefit long-term research. However, it is important to know if the spectral features of Landsat 8 are of the same standard as previous Landsat imagery because Landsat 8 images have narrower bands, especially because of the normalized difference vegetation index (NDVI) calculation which is the most popular vegetation index. In this study NDVI values derived from Landsat 8 images were compared with those calculated from Landsat 7 and ground measured hyperspectral data. The result shows that Landsat 8 NDVI is larger than Landsat 7 NDVI in lower vegetation covered areas and the difference becomes smaller as the value of NDVI increases. This indicates that NDVI of Landsat 7 and Landsat 8 is consistent when dealing with high vegetation covered areas (e.g. forest area and tall grass prairie) because the difference between Landsat 7 and 8 NDVI is close to zero when the value of NDVI is high, but this needs to be further investigated. There is also further need for calibration of NDVI in low vegetation covered areas in order to achieve consistency between Landsat 7 and Landsat 8 images.
Abstract:The mixed grassland in Canada is characterized by low to medium green vegetation cover, with a large amount of canopy background, such as non-photosynthetic vegetation residuals (litter), bare soil, and ground level biological crust. It is a challenge to extract the canopy information from satellite images because of the influence of canopy background. Therefore, this study aims to extract a soil line, a representation of bare soil with litter and soil crust in the surface, from Landsat images to reduce the background effect. Field work was conducted in the West Block of Grasslands National Park (GNP) in Canada, which represents the northern mixed grassland from late June to early July 2005. Six TM images with either no or only a small amount of cloud content were collected in 2005. In this study, soil lines were extracted directly from images by quantile regression and the (R, NIR min ) method. The results show that, (1) both cloud and cloud shadow have obvious influence on simulating soil line automatically from images; (2) green up and late senescence seasons are relatively better for soil line simulation; (3) the (R, NIR min ) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.
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