Spatial-temporal dynamics of China's terrestrial biodiversity: A dynamic habitat index diagnostic

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001-2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-cum 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-min) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-sea); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-min~0.05 and DHI-sea~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China's terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI. © 2016 by the authors

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified

QUANTIFICATION OF ERROR IN AVHRR NDVI DATA

Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown

Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan

Biomass is an important ecological variable for understanding the responses of vegetation to the currently observed global change. The impact of changes in vegetation biomass on the global ecosystem is also of high relevance. The vegetation in the arid and semi-arid environments of Kazakhstan is expected to be affected particularly strongly by future climate change. Therefore, it is of great interest to observe large-scale vegetation dynamics and biomass distribution in Kazakhstan. At the beginning of this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. Remote-sensing-based Net Primary Productivity (NPP) models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass estimates based on NPP data. For validation of the results, biomass field data were collected in three study areas in Kazakhstan. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM). The second is the Biosphere Energy Transfer Hydrology Model (BETHY/DLR). Both models were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR was then used to calculate NPP for Kazakhstan for 2003–2011. The results were analysed regarding spatial, intra-annual, and inter-annual variations. In addition, the correlation between NPP and meteorological parameters was analysed. In the last part of this dissertation, a methodological concept for derivation of above-ground biomass estimates of natural vegetation from NPP time-series has been developed. The concept is based on the NPP time-series, information about fractional cover of herbaceous and woody vegetation, and plants’ relative growth rates (RGRs). It has been the first time that these parameters are combined for biomass estimation in semi-arid regions. The developed approach was finally applied to estimate biomass for the three study areas in Kazakhstan and validated with field data. The results of this dissertation provide information about the vegetation dynamics in Kazakhstan for 2003–2011. This is valuable information for a sustainable land management and the identification of regions that are potentially affected by a changing climate. Furthermore, a methodological concept for the estimation of biomass based on NPP time-series is presented. The developed method is potentially transferable. Providing that the required information regarding vegetation distribution and fractional cover is available, the method will allow for repeated and large-area biomass estimation for natural vegetation in Kazakhstan and other semi-arid environments

Development and Extrapolation of a General Light Use Efficiency Model for the Gross Primary Production

The global carbon cycle is one of the large biogeochemical cycles spanning all living and non-living compartments of the Earth system. Against the background of accelerating global change, the scientific community is highly interested in analyzing and understanding the dynamics of the global carbon cycle and its complex feedback mechanism with the terrestrial biosphere. The international network FLUXNET was established to serve this aim with measurement towers around the globe. The overarching objective of this thesis is to exploit the powerful combination of carbon flux measurements and satellite remote sensing in order to develop a simple but robust model for the gross primary production (GPP) of vegetation stands. Measurement data from FLUXNET sites as well as remote sensing data from the NASA sensor MODIS are exploited in a data-based model development approach. The well-established concept of light use efficiency is chosen as modeling framework. As a result, a novel gross primary production model is established to quantify the carbon uptake of forests and grasslands across a broad range of climate zones. Furthermore, an extrapolation scheme is derived, with which the model parameters calibrated at FLUXNET sites can be regionalized to pave the way for spatially continuous model applications

Comparison of MODIS-Algorithms for Estimating Gross Primary Production from Satellite Data in semi-arid Africa

The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the two biophysical factors, Leaf Area Index (LAI) and absorbed Fraction of Photosynthetically Active Radiation (FPAR). The comparison is in their ability to estimate intra-annual variations of Gross Primary Production (GPP); this is done using the time-series data of quality screened eddy covariance (EC) Flux Tower stations from the Carbo Africa network as truth data. The results show a modest agreement between the different vegetation metrics and EC Flux Tower derived GPP, with an overall average coefficient of determination (R2) of 0.63 for LAI, R2 of 0.51 for NDVI, R2 of 0.52 FPAR and a R2 of 0.49 for EVI, using all stations and years of data. When each station received the same weight, i.e. using the correlation of all observation for each station and then calculating the average, the overall correlation improved, still showing LAI as the best predictor of Flux Tower GPP with a R2 of 0.62, but with an improved EVI with a R2 of 0.61, while NDVI and FPAR had an R2 of 0.57 and 0.59 respectively. This result and the observed large variation in between stations, e.g. NDVI between an R2 of 0.62 and 0.83 for the station Demokeya compared to an R2 of 0.32 and 0.49 of NDVI for the station Tchizalamou, may indicate a site specific proficiency of the vegetation metrics. When the observations within the growing period were tested separately a strong decrease in correlation was observed, with an average R2 between 0.41 – 0.56 for all station and years and an average R2 between 0.36 – 0.45 for all sites using all observations for each station regardless of year, lending strength to the assumption that the non-vegetation period observations affect the correlation greatly. The study concludes that up scaling of an intra-annual standardized major axis regression model based solely on the relationship between any of these metrics and Flux Tower estimated GPP is inadvisable due to the modest overall intra-annual agreement between the metrics and GPP. It is also concluded that since the vegetation metrics display site specific proficiency, models of GPP would benefit from site specific ancillary data that describes vegetation-limiting factors, e.g. water availability.Klimatmönstren världen över förändras och med dessa den globala distributionen och mängden av landbundet kol, dvs vegetationen som bland annat nyttjas som mat och fiber. Också I sig självt en viktig faktor i klimatets utveckling genom dess roll i energi- och vattenkretsloppen. Vetskap om kvantitet och distribution av landbundet kol och hur detta förändras är en viktigt del av arbetet i att förstå hur de globala mönster förändras, och för denna avsikt har bredskaliga modeller som nyttjar satellit data framtagits. Denna studie jämför fyra vegetations relaterade MODIS (Moderate Resolution Imaging Spectroradiometer) produkter, som erhålls från MODIS satellit data genom algoritmer som kalkylerar de två vegetation indexen, Normalized Vegetation Index (NDVI) och Enhanced Vegetation Index (EVI), och de två biofysiska faktorerna, Leaf Area Index (LAI) och absorbed Fraction of Photosynthetically Active Radiation (FPAR). Deras förmåga att uppskatta variationen av den totala primär produktionen (Gross Primary Production, GPP) över året jämförs, genom tidsserier av eddy kovarians (EC) data från flux torn ur Carbo Africa nätverket, vars tidsserie-utveckling används som sanningspunkter varmot variationen från de motsvarande tidsserierna av algoritmerna jämförs. Resultatet visar en blygsam korrelation mellan de olika vegetations algoritmernas reslutat och EC flux torn uppskattat GPP, med ett medel av determinationskoefficienter (R2) på 0.49 för EVI, 0.51 för NDVI,0.52 för FPAR och ett R2 på 0.63 för LAI, då data från alla stationer och år användes. När var station erhöll lika stor vikt, dvs då korrelationen kalkylerades för samtliga observationer från var station, varpå medel togs fram, förbättrades korrelationen över lag. fLAI visades fortfarande som den bästa prediktorn av flux-torns uppskattad GPP med ett R2 på 0.62, ett starkt förbättrat R2 för EVI på 0.61erhölls, medans NDVI och FPAR visa ett R2 på 0.57 respektive 0.59. Detta resultat och en stundtals stor variation mellan stationer, t.ex. NDVI med ett R2 mellan 0.62 och 0.83 för stationen Demokeya jämfört med ett R2 mellan 0.32 och 0.49 för NDVI och stationen Tchizalamou, visar kanske på plats specifika förmågor hos vegetations algoritmerna. När observationer inom vegetationsperioden testades separat observerades en starkt minskad korrelation, med ett medel R2 mellan 0.41 – 0.56 för alla stationer och år, och ett R2 mellan 0.36 – 0.45 för alla platser vid användning av samtliga observationer för varje station oberoende av år, vilket indikerar att observationerna utanför växtperioden har stort inflytande på korrelationen. En slutsats av studien är att uppskalning av en standrardiserad storaxels regressions modell för inom annuell variation baserad endast på relationen mellan en av dessa vegetations algoritmer och flux-torns uppskattad GPP ej är att rekommendera med tanke på den blygsamma överrensstämmelsen mellan vegetationsalgoritmerna och flux torn uppskattat GPP. En annan slutsats är att eftersom dessa vegetations algoritmer uppvisar plats specifika förmågor skulle modeller av GPP ha fördel av plats specifik stöd-data som beskriver faktorer som begränsar vegetation, t.ex. vattentillgänglighet

Land Ecosystems and Hydrology

The terrestrial biosphere is an integral component of the Earth Observing System (EOS) science objectives concerning climate change, hydrologic cycle change, and changes in terrestrial productivity. The fluxes o f CO2 and other greenhouse gases from the land surface influence the global circulation models directly, and changes in land cover change the land surface biophysical properties o f energy and mass exchange. Hydrologic cycle perturbations result from terrestrially-induced climate changes, and more directly from changes in land cover acting on surface hydrologic balances. Finally, both climate and hydrology jointly control biospheric productivity, the source o f food, fuel, and fiber for humankind. The role of the land system in each of these three topics is somewhat different, so this chapter is organized into the subtopics of Land-Climate, Land-Hydrology, and Land-Vegetation interactions (Figures 5.1, 5.2, and 5.3)

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI \u3c 2m2/m2, AGB \u3c 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI \u3e 2m2/m2, AGB \u3e 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management