Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years

Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data

Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003–2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model’s application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances

Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data

Accurate quantification of gross primary production (GPP) at regional and global scales is essential for carbon budgets and climate change studies. Five models, the vegetation photosynthesis model (VPM), the temperature and greenness model (TG), the alpine vegetation model (AVM), the greenness and radiation model (GR), and the MOD17 algorithm, were tested and calibrated at eight sites in China during 2003-2005. Results indicate that the first four models provide more reliable GPP estimation than MOD17 products/algorithm, although MODIS GPP products show better performance in grasslands, croplands, and mixed forest (MF). VPM and AVM produce better estimates in forest sites (R-2 = 0.68 and 0.67, respectively); AVM and TG models show satisfactory GPP estimates for grasslands (R-2 = 0.91 and 0.9, respectively). In general, the VPM model is the most suitable model for GPP estimation for all kinds of land cover types in China, with R2 higher than 0.34 and root mean square error (RMSE) lower than 48.79%. The relationships between eddy CO2 flux and model parameters (Enhanced Vegetation Index (EVI), photosynthetically active radiation (PAR), land surface temperature (LST), air temperature, and Land Surface Water Index (LSWI)) are further analyzed to investigate the model's application to various land cover types, which will be of great importance for studying the effects of climatic factors on ecosystem performances

An integrated approach to grassland productivity modelling using spectral mixture analysis, primary production and Google Earth Engine

Thesis (MA)--Stellenbosch University, 2020.ENGLISH ABSTRACT: Grassland degradation can have a severe impact on condition, productivity and consequently grazing potential. Current conventional methods for monitoring and managing grasslands are time-consuming, destructive and not applicable at large-scale. These constraints could be addressed using a remote sensing (RS)-based approach, however, current RS-based approaches also have technological and scientific limitations in the context of grassland management. The inability of RS-based primary production models to discriminate between herbaceous and woody production at sub-pixel level poses constraints for use in grazing capacity (GC) calculation. The integration of fractional vegetation cover (FVC) is posed as a promising solution, specifically estimation using spectral mixture analysis (SMA). Current grassland monitoring approaches are limited by the technological constraints of traditional, desktop-based RS approaches, but the implementation of analysis in a Google Earth Engine (GEE) web application can address these limitations by providing dynamic, continuous productivity estimates. Field data collection and analysis of biophysical parameters were performed to establish crucial relationships between vegetation productivity and RS signals. Biophysical parameters obtained include FVC, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and grass dry matter (DM) production. An important outcome was the improvement of the normalised difference vegetation index (NDVI) and fAPAR regression relationship, achieved by scaling fAPAR using the proportion of green, living biomass. The relationship proved useful in subsequent vegetation productivity modelling. The potential of SMA for FVC estimation using medium resolution imagery (Landsat 8 and Sentinel-2) and relatively few field points, was explored. A linear spectral mixture model (LSMM) was calibrated, implemented and evaluated on accuracy and transferability. A number of bands and spectral indices were identified as core features, specifically the dry bare-soil index (DBSI). DBSI improved discrimination between bare ground and dry vegetation, a common challenge in semi-arid conditions. The calibrated LSMM performed well, with Sentinel-2 providing the most accurate results. The research proved the transferability of the LSMM approach, as accurate FVC estimates were obtained for both arid, dry season conditions and green, growing season conditions. The LSMM-estimated FVC was combined with primary production to improve GC calculation for grassland and rangelands. Annual grassland production was calculated using the Regional Biosphere Model (RBM). Although a water stress factor is a well-known source of uncertainty, the research found its inclusion crucial to the transferability of the model between different climatic conditions. FVC was used to determine the grazable primary production from RBM estimates, thus mitigating the effects of woody components on GC calculations. A comparison of model-estimated GC to the most recent national GC map showed good agreement. Slight discrepancies were likely due to the inability of the model to include species composition and palatability in GC calculations. The final FVC-integrated productivity model was implemented in a GEE web app to demonstrate the practical contribution of the research for continuous, dynamic, multi-scale and sustainable grassland management. Overall, the findings of the research provide valuable insights into improving RS-based modelling of grassland condition and productivity. Operationalisation of this research can aid in identifying potential degradation, highlighting regions vulnerable to food shortages and establishing sustainable productivity levels. Recommendations include investigating alternative methods for estimating water stress and exploring the incorporation of species composition in GC calculation using RS.AFRIKAANSE OPSOMMING: Agteruitgang van grasvelde kan 'n ernstige invloed op kondisie, produktiwiteit en gevolglik weidingspotensiaal hê. Huidige konvensionele metodes vir die monitering en bestuur van grasvelde is tydrowend, vernietigend en nie op groot skaal toepasbaar nie. Hierdie beperkinge kan met behulp van 'n afstandwaarnemings (AW)-gebaseerde benadering aangespreek word, maar huidige AW-metodes het egter ook tegnologiese en wetenskaplike beperkings, veral in die konteks van veldbestuur. Die onvermoë van AW-gebaseerde primêre produksiemodelle om tussen kruidagtige en houtagtige produksie op sub-pixelvlak te onderskei, hou beperkings in vir die berekening van drakapasiteit (DK). Die integrasie van fraksionele plantegroeibedekking (FPB) word aangebied as 'n belowende oplossing. Beraming van FPB deur gebruik te maak van spektrale mengselanalise (SMA) het veral potensiaal. Huidige benaderings vir die monitering van grasvelde word beperk deur die tegnologiese beperkings van tradisionele, rekenaargebaseerde AW-metodes, maar die implementering van analise in 'n Google Earth Engine (GEE) webtoepassing kan hierdie beperkings aanspreek deur dinamiese, deurlopende produktiwiteitsramings te verskaf. Velddata is ingesamel en analise van biofisiese parameters is uitgevoer om belangrike verwantskappe tussen plantproduktiwiteit en AW-seine te bepaal. Die biofisiese parameters sluit in FPB, blaaroppervlakte-indeks (BOI), fraksie van geabsorbeerde fotosinteties aktiewe bestraling (fAFAB) en droë materiaal (DM) produksie. Die verbetering van die genormaliseerde verskilplantegroei-indeks (NVPI) en fAFAB -regressie-verhouding, wat verkry is deur fAFAB te skaleer met behulp van die hoeveelheid groen, lewende biomassa was ‘n belangrike uitkoms. Die verwantskap was nuttig in die daaropvolgende modellering van plantegroei. Die potensiaal van SMA vir die bepaling van FPB deur middel van medium resolusiebeelde (Landsat 8 en Sentinel-2) met relatief min veldpunte is ondersoek. 'n Lineêre spektrale mengelmodel (LSMM) is gekalibreer, geïmplementeer en vir akkuraatheid en oordraagbaarheid geëvalueer. 'n Aantal bande en spektrale indekse is as kernkenmerke geïdentifiseer, spesifiek die droë kaal-grondindeks (DKGI). DKGI het die onderskeid tussen kaal grond en droë plantegroei, 'n algemene uitdaging in semi-droë landskappe, verbeter. Die gekalibreerde LSMM het goed gevaar, met Sentinel-2 wat die akkuraatste resultate gelewer het. Die navorsing het bewys dat die LSMM-benadering oorgedra kan word, aangesien akkurate FPB-ramings vir beide droë seisoen en groen, groeiseisoen toestande verkry is. Die LSMM-beraamde FPB is met primêre produksie ramings gekombineer om die DK-berekening vir grasveld te verbeter. Die jaarlikse grasveldproduksie is met behulp van die Streeks Biosfeer Model (SBM) bereken. Alhoewel 'n waterstresfaktor 'n bron van onsekerheid is, het die navorsing bevind dat dit die gebruik daarvan vir die oordraagbaarheid van die model tussen verskillende klimaatstoestande belangrik is. FPB is gebruik om die weibare primêre produksie volgens SBMramings te bepaal, en het die effekte van houtagtige komponente op DK-berekeninge verminder. 'n Vergelyking van die gemodelleerde DK met die nuutste nasionale DK-kaart het 'n goeie ooreenkoms getoon. Klein afwykings was waarskynlik te wyte aan die onvermoë van die model om spesiesamestelling en eetbaarheid by DK-berekeninge in te sluit. Die finale FPB-geïntegreerde produktiwiteitsmodel is in 'n GEE webtoep geïmplementeer om die praktiese bydrae van die navorsing vir deurlopende, dinamiese, meervoudige en volhoubare grasveldbestuur te demonstreer. In die geheel bied die bevindinge van die navorsing waardevolle insigte in die verbetering van die AW-gebaseerde modellering van veldtoestand en produktiwiteit. Operasionalisering van hierdie navorsing kan tot die identifisering van potensiële agteruitgang, die uitlig van streke wat kwesbaar is vir voedseltekorte en die bepaling van volhoubare produktiwiteitsvlakke bydra. Aanbevelings sluit in die ondersoek van alternatiewe metodes vir die beraming van waterstres en die gebruik van spesiesamestelling in DK-berekening met behulp van AW.Master

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects