Understanding moisture stress on light use efficiency across terrestrial ecosystems based on global flux and remote-sensing data

2015 ◽  
Vol 120 (10) ◽  
pp. 2053-2066 ◽  
Author(s):  
Yulong Zhang ◽  
Conghe Song ◽  
Ge Sun ◽  
Lawrence E. Band ◽  
Asko Noormets ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Moisture Stress ◽  
Light Use Efficiency ◽  
Sensing Data ◽  
Use Efficiency

scholarly journals Indicative landscape monitoring of the national nature park (case study the territory of Slobozhansky National Nature Park)

2018 ◽  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Gis Technology ◽  
Landscape Indicators ◽  
Monitoring Method ◽  
Sensing Data ◽  
The Usa ◽  
Nature Park ◽  
Landscape Monitoring

Formulation of the problem. National Natural Parks (NNP) – protected areas where anthropogenic and natural landscapes are combined in the same territory. In addition, the main functions of such objects are significantly competitive, which requires monitoring of changes in existing landscapes. It is necessary to define the local objects which, being the most sensitive, at the same time have small plasticity, therefore, are capable to react quickly and adequately to any changes. That is what we call indicative. Analysis of recent research and publications. Many researchers of the USA, Great Britain, Germany, Australia conduct landscape monitoring using remote sensing data and GIS technologies. For example, D. Keith, S. Rodoreda, L. Holman, R. Noss, U. Walz, and others. The National Inventory of Landscapes in Sweden studies development of modern landscape monitoring in countries of Europe. Landscape Monitoring of Terrestrial Ecosystems, studied by researches R. Kennedy, J. Jons, K. Jones and others allow using data of satellite for selection of plant contours using Gis-technology. Landscape monitoring of the territory of NNP «Slobozhanskiy» has never been carried out. The aim of the study is to choose satellite images, taking into account the area of the study, the choice of optimal methods of their processing for the compilation of a database of landscape structure facies for landscape monitoring based on long-term observations on the ground, comparing their results with geodata. We have determined wetlands, as landscape indicators. Presentation of the main material of the study. Comprehensive analysis of remote sensing data carried out by the authors, allowed us to make sure that vegetation cover is the most indicative, except for the contours of wetlands, which are clearly identified and easily compared in multi-spectral images. It is reliably determined by the characteristic features combine with the corresponding spectral ranges and the image structure. In addition, changes in vegetation allows you to visually determine changes in landscape groupings and the speed of these changes. Summary. The indicative features of landscape monitoring are wetlands, and there are two direct indicators: the contours of wetlands and the change in the aspect of vegetation. The monitoring method is a multispectral analysis of images obtained by processing combinations of spectral channels, which showed the ability to determine the changes in the selection, taking into account reflectivity of the surface. Limitations of the method are the following: there is no established method of meticulous analysis of changes in the structure of vegetation, which is observed visually, but is not reflected instrumentally; inability to take into account random features of the territory conditions and space scanning at a certain point, which is interesting for the study. Finally, the types of monitoring objects, indicative signs of changes and ways to track them according to high-precision and generally available satellite information are determined.

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

scholarly journals Detection of Changes in Terrestrial Ecosystems of Ukraine Using Remote Sensing Data

2020 ◽  
Vol 29 (1) ◽  
pp. 102-110 ◽  
Author(s):  
Vadym I. Lyalko ◽  
Inna F. Romanciuc ◽  
Lesia A. Yelistratova ◽  
Aleksandr A. Apostolov ◽  
Viktor M. Chekhniy
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Moisture Content ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Quantitative Information ◽  
Extreme Weather Events ◽  
Natural Soil ◽  
Sensing Data

In recent years, Ukraine has been affected by climate change. This has led to frequent extreme weather events (heavy / high rains, floods, droughts, squalls). As a result of droughts, desertification is one of the most dangerous and transient consequences of modern climate change. The research is devoted to the diagnostic assessment of the modern climate of Ukraine. Remote sensing data and instrumental observations of 30 weather stations of Ukraine were used. Temperature increase was registered in the study area by all stations, which significantly affected the level of precipitation. At the moment there is not enough moisture for the Earth’s surface. Precipitation in Ukraine is currently characterized by an uneven distribution. It leads to accelerated processes of soil degradation and it’s fertility loss. The aim of the study was to identify areas prone to desertification using satellite imagery and meteorological observations. Over the past 17 years (2000-2017), the average air temperature in Ukraine has increased by 1.5 ºC. Particularly anomalous warming has been recorded in recent years, starting in 2015. During the XXI century, a slight decrease in precipitation was observed in Ukraine. Both a decrease in precipitation and an increase in temperature may lead to a decrease in soil moisture levels. According to ground meteorological data, the tendency of dryness in Ukraine was confirmed. Lack of water leads to prompt manifestation of this process. Water indexes were used to estimate the moisture content of surface soils. It is possible to assess the susceptibility of the desert area to climate change. Relevant quantitative information on water availability in Ukraine is provided. Two water indices (Normalized Difference Infrared Index NDII and Ratio Drought Index RDI) have been taken estimate the moisture content. It can be estimated from the MODIS MOD13C2 product data obtained from the MODIS satellite sensor and used for regional research. The main conclusion of this study is to determine the changes in natural terrestrial ecosystems in Ukraine. This was shown on the basis of temperature and humidity. Such trends may lead to changes in the biodiversity of the territory and loss of natural soil properties.

scholarly journals Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems

2016 ◽  
Vol 40 (2) ◽  
pp. 322-351 ◽  
Author(s):  
Jadunandan Dash ◽  
Booker O. Ogutu
Keyword(s):  
Remote Sensing ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Data Availability ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Ecosystem Monitoring ◽  
Sensing Data

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.

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