Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

A Global Assessment of Long-Term Greening and Browning Trends in Pasture Lands Using the GIMMS LAI3g Dataset

Pasture ecosystems may be particularly vulnerable to land degradation due to the high risk of human disturbance (e.g., overgrazing, burning, etc.), especially when compared with natural ecosystems (non-pasture, non-cultivated) where direct human impacts are minimal. Using maximum annual leaf area index (LAImax) as a proxy for standing biomass and peak annual aboveground productivity, we analyze greening and browning trends in pasture areas from 1982-2008. Inter-annual variability in pasture productivity is strongly controlled by precipitation (positive correlation) and, to a lesser extent, temperature (negative correlation). Linear temporal trends are significant in 23% of pasture cells, with the vast majority of these areas showing positive LAImax trends. Spatially extensive productivity declines are only found in a few regions, most notably central Asia, southwest North America, and southeast Australia. Statistically removing the influence of precipitation reduces LAImax trends by only 13%, suggesting that precipitation trends are only a minor contributor to long-term greening and browning of pasture lands. No significant global relationship was found between LAImax and pasture intensity, although the magnitude of trends did vary between cells classified as natural versus pasture. In the tropics and Southern Hemisphere, the median rate of greening in pasture cells is significantly higher than for cells dominated by natural vegetation. In the Northern Hemisphere extra-tropics, conversely, greening of natural areas is 2-4 times the magnitude of greening in pasture areas. This analysis presents one of the first global assessments of greening and browning trends in global pasture lands, including a comparison with vegetation trends in regions dominated by natural ecosystems. Our results suggest that degradation of pasture lands is not a globally widespread phenomenon and, consistent with much of the terrestrial biosphere, there have been widespread increases in pasture productivity over the last 30 years

Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.Tertiary Education Trust Fund, Nigeri

Maanpäällisten hiilivarastojen kvantifiointi : vertaileva kirjallisuuskatsaus

Ihmisen toiminta on vaikuttanut hiilen kiertokulkuun. Maalla sijaitsevien hiilivarastojen koko on pienentynyt, kun samaan aikaan ilmakehän hiilidioksidipitoisuus on kasvanut aiheuttaen ilmaston lämpenemistä. Pariisin ilmastosopimuksen tavoite on rajoittaa lämpeneminen alle 1,5 asteeseen ja tähän tavoitteeseen pääsemiseksi tarvitaan useita eri keinoja. Yksi mahdollinen keino on hiilensidonta maaperään ja kasvillisuuteen. Jotta luonnolliset hiilivarastot saadaan tehokkaasti mukaan EU -politiikkaan, tulee varastoihin sitoutuneen hiilen määrä pystyä kvantifioimaan. Kehittyviä hiilimarkkinoita varten menetelmän tulee olla tarkka, käytännöllinen ja taloudellisesti mahdollinen. Tämän tutkimuksen tutkimusmenetelmä oli vertaileva kirjallisuuskatsaus ja tavoitteena oli kerätä ja vertailla tietoa tällä hetkellä käytössä olevista menetelmistä hiilen varastojen koon määrittämiseen. Maaperän hiilivarastojen koko pystytään kvantifioimaan maaperänäytteillä, erilaisten maaperäantureiden ja mallinnuksen avulla. Kasvillisuuteen sitoutuneen hiilen määrää voidaan kvantifioida inventaarioperusteisesti tai kaukokartoituksen avulla. Koko ekosysteemin hiilibudjetti saadaan määritettyä kaasuvuo mittauksilla. Maaperän ja kasvillisuuden hiilivarastojen koon ja varaston muutoksen määrittäminen ei ole yksinkertainen tehtävä. Varastojen välillä on runsaasti vaihtelua ja varastoissa tapahtuvat muutokset ovat osittain erittäin hitaita. Varastojen koon määrittämisen kustannukset vaihtelevat tarkkuuden, varaston koon ja toimenpiteiden mukaan. Menetelmät ovat myös riippuvaisia hyvälaatuisesta tiedosta, minkä vuoksi erilaiset tutkimukset hiiliensidontaan vaikuttavista tekijöistä ovat välttämättömiä. Tällä hetkellä hiilimarkkinoita ajatellen erilaiset mallinukseen pohjautuvat menetelmät ovat kustannustehokkaita, läpinäkyviä ja toistettavia, minkä vuoksi niitä voitaisiin hyödyntää jo nyt. Ei ole kuitenkaan olemassa täydellistä mallia, eikä yhtä vaihtoehtoa kaikkiin tilanteisiin, minkä vuoksi eri mallien toiminta tulee olla todennettua. Tämän lähestymistavan myötä voisi kuitenkin olla mahdollista saada enemmän pieniä toimijoita hiilensidonnan markkinoille ja siten tehostaa ilmastotoimia. Eri menetelmät tuovat erilaista tietoa ja myös mallinnukseen pohjautuva lähestymistapa on riippuvainen empiriasta, minkä vuoksi olisi tärkeää, että kaikki informaatio saadaan hyödynnettyä menetelmien kehittämisessä ja tarkkuuksien parantamisessa.The natural carbon cycle is affected by human activity. Terrestrial carbon stocks have been decreasing as at the same time carbon dioxide concentration in the atmosphere has increased causing climate change. The Paris Agreement sets the target to limit climate change to 1.5°C and to reach that goal, all possible mitigation practises should be included into global framework to avoid the most serious consequences of warming. Carbon sequestration into natural soil and biomass could be one mitigation practice. To enhance carbon sequestration activities and to include natural carbon stocks into to the EU climate policy, it would be necessary to quantify stock sizes and changes in those stocks. For developing carbon trading markets, the quantification methods should provide accurate results and at the same time be practical and financially achievable. Used research method in this thesis was comparatively literature survey and aim was to gather and compere information about currently used carbon stock quantification methods against developing carbon trading markets. Soil carbon stocks can be quantified with direct soil sampling, spectroscopic sensing methods or by mathematical models. Biomass carbon stocks can be quantified with inventory-based field measurements and modelling and by remote sensing. The full carbon budget on the ecosystem level can be achieved with carbon flux measurements. Quantification of different terrestrial carbon stocks and their changes is not a simple task. There is a lot of variation between different stocks and in some cases, the stock changes occur slow. Cost of carbon stock quantification depends on the accuracy, size of the area under focus and frequency of the measures. Methods for terrestrial carbon stock quantification are dependent on high quality data and there is demand for research considering carbon sequestration. For carbon offsetting purposes of developing carbon markets, the modelling approach is achievable, cost efficient, repeatable and transparent. There is no perfect model or one universal model that would fit to every situation and thus the differences must be known. At this stage, this approach could be one possibility to include small scale projects and enhance climate actions. Different quantification methods provide information which can be used to different method developments and to increase accuracies. It’s important to know, how all information can be effectively utilized

Long-term satellite NDVI data sets: evaluating their ability to detect ecosystem functional changes in South America

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMIMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.Fil: Baldi, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Aragón, Myriam Roxana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Aversa, Fernando. Universidad Nacional de San Luis; ArgentinaFil: Paruelo, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentin

Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale

Eddy covariance (EC) measurements are often used to validate net ecosystem productivity (NEP) estimated from satellite remote sensing data and biogeochemical models. However, EC measurements represent an integrated flux over their footprint area, which usually differs from respective model grids or remote sensing pixels. Quantifying the uncertainties of scale mismatch associated with gridded flux estimates by upscaling single EC tower NEP measurements to the grid scale is an important but not yet fully investigated issue due to limited data availability as well as knowledge of flux variability at the grid scale. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) built a flux observation matrix that includes 17 EC towers within a 5 km × 5 km area in a heterogeneous agricultural landscape in northwestern China, providing an unprecedented opportunity to evaluate the uncertainty of upscaling due to spatial representative differences at the grid scale. Based on the HiWATER-MUSOEXE data, this study evaluated the spatial representativeness and uncertainty of EC CO2 flux measurements for upscaling to the grid scale using a scheme that combines a footprint model and a model-data fusion method. The results revealed the large spatial variability of gross primary productivity (GPP), ecosystem respiration (Re), and NEP within the study site during the growing season from 10 June to 14 September 2012. The variability of fluxes led to high variability in the representativeness of single EC towers for grid-scale NEP. The systematic underestimations of a single EC tower may reach 92(±11)%, 30(±11)%, and 165(±150)% and the overestimations may reach 25(±14)%, 20(±13)%, and 40(±33)% for GPP, Re, and NEP, respectively. This finding suggests that remotely sensed NEP at the global scale (e.g., MODIS products) should not be validated against single EC tower data in the case of heterogeneous surfaces. Any systematic bias should be addressed before upscaling EC data to grid scale. Otherwise, most of the systematic bias may be propagated to grid scale due to the scale dependence of model parameters. A systematic bias greater than 20% of the EC measurements can be corrected effectively using four indicators proposed in this study. These results will contribute to the understanding of spatial representativeness of EC towers within a heterogeneous landscape, to upscaling carbon fluxes from the footprint to the grid scale, to the selection of the location of EC towers, and to the reduction in the bias of NEP products by using an improved parameterization scheme of remote-sensing driven models, such as VPRM

Quantifying the reliability of four global datasets for drought monitoring over a semiarid region

Drought is one of the most relevant natural disasters, especially in arid regions such as Iran. One of the requirements to access reliable drought monitoring is long-term and continuous high-resolution precipitation data. Different climatic and global databases are being developed and made available in real time or near real time by different agencies and centers; however, for this purpose, these databases must be evaluated regionally and in different local climates. In this paper, a near real-time global climate model, a data assimilation system, and two gridded gauge-based datasets over Iran are evaluated. The ground truth data include 50 gauges from the period of 1980 to 2010. Drought analysis was carried out by means of the Standard Precipitation Index (SPI) at 2-, 3-, 6-, and 12-month timescales. Although the results show spatial variations, overall the two gauge-based datasets perform better than the models. In addition, the results are more reliable for the western portion of the Zagros Range and the eastern region of the country. The analysis of the onsets of the 6-month moderate drought with at least 3 months’ persistence indicates that all datasets have a better performance over the western portion of the Zagros Range, but display poor performance over the coast of the Caspian Sea. Base on the results of this study, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset is a preferred alternative for drought analysis in the region when gauge-based datasets are not available

Development of tools for water management in the Hatra watershed (Northwestern Iraq) using satellite technologies

“All around the world the demand for water is increasing, especially in arid and semi-arid regions, including Iraq which subject to continuous desertification that is worsening, more importantly the Jezira region in northwestern Iraq. Thus, it’s crucial to have a better strategy for water management. One of these strategies is to promote groundwater recharge for restoring the aquifer depletion. The successful groundwater recharge is limited by some potential data such as the annual water budge and precipitation measurements. The atomospheric and hydrological observations are limited by sparse population which tends to be less in arid and semi-arid regions. Therefore, an alternative to the ground measurement of rainfall is needed. Satellite-based measurements limit the restriction of ground station. However, the satellite products have significant uncertainty. Therefore, seven precipitation estimates have tested against rain gauges in Orange County and Los Angeles County, California. In order to establish a water management strategy in Jezira region, annual water budget should be known, which could be measure through observational discharge station. Unfortunately, only few months of discharge was measured manually in the north Jezira, which Hatra subwatershed. Computer model was used to recover the streamflow measurement. The Soil and Water Assessment Tool (SWAT) was great candidate to overcome the problem. The observational data of stream discharge was used to calibrate the model. In conclusion, water management is possible in ungauged arid and semi-arid regions by using remote sensing data and computer modeling”--Abstract, page iv