Response of CO₂ and H₂O fluxes in a mountainous tropical rainforest in equatorial Indonesia to El Niño events

The possible impact of El Niño–Southern Oscillation (ENSO) events on the main components of CO₂ and H₂O fluxes between the tropical rainforest and the atmosphere is investigated. The fluxes were continuously measured in an old-growth mountainous tropical rainforest in Central Sulawesi in Indonesia using the eddy covariance method for the period from January 2004 to June 2008. During this period, two episodes of El Niño and one episode of La Niña were observed. All these ENSO episodes had moderate intensity and were of the central Pacific type. The temporal variability analysis of the main meteorological parameters and components of CO₂ and H₂O exchange showed a high sensitivity of evapotranspiration (ET) and gross primary production (GPP) of the tropical rainforest to meteorological variations caused by both El Niño and La Niña episodes. Incoming solar radiation is the main governing factor that is responsible for ET and GPP variability. Ecosystem respiration (RE) dynamics depend mainly on the air temperature changes and are almost insensitive to ENSO. Changes in precipitation due to moderate ENSO events did not have any notable effect on ET and GPP, mainly because of sufficient soil moisture conditions even in periods of an anomalous reduction in precipitation in the region

The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics

Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net/

Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale

Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia

Open-Access-Publikationsfonds 201

Spatiotemporal trends of forest cover change in Southeast Asia

© 2010, Springer-Verlag Berlin Heidelberg. The current state of tropical forest cover and its change have been identified as key variables in modelling and measuring the consequences of human action on ecosystems. The conversion of tropical forest cover to any other land cover (deforestation) directly contributes to the two main environmental threats of the recent past: 1) the alteration of the global climate by the emission of carbon to the atmosphere and 2) the decline in tropical biodiversity by land use intensification and habitat conversion. The sub-continent of Southeast Asia exhibits one of the highest rates of forest loss and comprises one of the regions with the highest amount and diversity of flora and fauna species, worldwide. The knowledge of the spatial and temporal trends in the variation of forest cover in tropical regions is a prerequisite for the development and establishment of mitigation strategies from the global to the regional level. However, there is considerable disagreement in recent estimates of tropical forest cover change ranging from continuing and intensified decline in forest loss to a distinct decrease in deforestation rates and up to stagnation in other cases. Against this background, the present study aims at a review and comparison of recently available global forest cover estimates for the region of Southeast Asia. In a case study, the results at the national level will be compared to an analysis at the regional level for the island of Sulawesi, Indonesia. The outcome of the study provides recommendations for future remote sensing based forest assessments in tropical regions

A remote sensing based monitoring system for discrimination between climate and human-induced vegetation change in Central Asia

Purpose – This paper aims to demonstrate the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter-annual rainfall variability. Design/methodology/approach – Linear regression models were used to determine trends in NDVI and precipitation and their interrelations for each pixel. Trends in NDVI that were entirely supported by precipitation trends were considered to impose climate-induced vegetation change. Trends in NDVI that were not explained by trends in precipitation were considered to mark human-induced vegetation change. Modelling results were validated by test of statistical significance and by comparison with the data from higher resolution satellites and fieldtrips to key test sites. Findings – More than 26 percent of all vegetated area in Central Asia experienced significant changes during 1981-2000. Rainfall has been proved to enforce most of these changes (21 percent of the entire vegetated area). The trends in vegetation activity driven by anthropogenic factor are much scarcer and occupy about 5.75 percent of the studied area. Practical implications – Planners, decision makers and other interest groups can use the findings of the study for assessment and monitoring land performance/land degradation over dry regions. Originality/value – The study demonstrates the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter-annual rainfall variability