Andrea Ehammer

Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data

Mapping anthropogenic forest disturbances has largely been focused on distinct delineations of events of deforestation using optical satellite images. In the tropics, frequent cloud cover and the challenge of quantifying forest degradation remain problematic. In this study, we detect processes of deforestation, forest degradation and successional dynamics, using long-wavelength radar (L-band from ALOS PALSAR) backscatter. We present a detection algorithm that allows for repeated disturbances on the same land, and identifies areas with slow- and fast-recovering changes in backscatter in close spatial and temporal proximity. In the study area in Madre de Dios, Peru, 2.3% of land was found to be disturbed over three years, with a false positive rate of 0.3% of area. A low, but significant, detection rate of degradation from sparse and small-scale selective logging was achieved. Disturbances were most common along the tri-national Interoceanic Highway, as well as in mining areas and areas under no land use allocation. A continuous spatial gradient of disturbance was observed, highlighting artefacts arising from imposing discrete boundaries on deforestation events. The magnitude of initial radar backscatter, and backscatter decrease, suggested that large-scale deforestation was likely in areas with initially low biomass, either naturally or since already under anthropogenic use. Further, backscatter increases following disturbance suggested that radar can be used to characterize successional disturbance dynamics, such as biomass accumulation in lands post-abandonment. The presented radar-based detection algorithm is spatially and temporally scalable, and can support monitoring degradation and deforestation in tropical rainforests with the use of products from ALOS-2 and the future SAOCOM and BIOMASS missions.

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVImin) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVImin as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVImin estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVImin derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVImin can serve as a proxy for assessing changes in tree cover. Good agreement (R2 = 0.79) between significant NDVImin trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVImin were registered by both MODIS and VGT dry season NDVImin time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVImin trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVImin derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.

Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery

The fraction of photosynthetically active radiation (FPAR) absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index (NDVI) for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels (∼70% crop cover), is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.

Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements

Land surface biophysical parameters such as the fraction of photosynthetic active radiation (fPAR) and leaf area index (LAI) are keys for monitoring vegetation dynamics and in particular for biomass and carbon flux simulation. This study aimed at deriving accurate regression equations from the newly available RapidEye satellite sensor to be able to map regional fPAR and LAI which could be used as inputs for crop growth simulations. Therefore, multi-temporal geo- and atmospherically corrected RapidEye scenes were segmented to derive homogeneous patches within the experimental fields. Various vegetation indices (VI) were calculated for each patch focusing on indices that include RapidEyes red edge band and further correlated with in situ measured fPAR and LAI values of cotton and rice. Resulting coefficients of determination ranged from 0.55 to 0.95 depending on the indices analysed, object scale, crop type and regression function type. The general relationships between VI and fPAR were found to be linear. Nonlinear models gave a better fit for VI-LAI relation. VIs derived from the red edge channel did not prove to be generally superior to other VIs.

Very high CO2 exchange fluxes at the peak of the rainy season in a West African grazed semi-arid savanna ecosystem

Abstract Africa is a sink of carbon, but there are large gaps in our knowledge regarding the CO2 exchange fluxes for many African ecosystems. Here, we analyse multi-annual eddy covariance data of CO2 exchange fluxes for a grazed Sahelian semi-arid savanna ecosystem in Senegal, West Africa. The aim of the study is to investigate the high CO2 exchange fluxes measured at the peak of the rainy season at the Dahra field site: gross primary productivity and ecosystem respiration peaked at values up to −48 μmol CO2 m−2 s−1 and 20 μmol CO2 m−2 s−1, respectively. Possible explanations for such high fluxes include a combination of moderately dense herbaceous C4 ground vegetation, high soil nutrient availability and a grazing pressure increasing the fluxes. Even though the peak net CO2 uptake was high, the annual budget of −229 ± 7 ± 49 g C m−2 y−1 (±random errors ± systematic errors) is comparable to that of other semi-arid savanna sites due the short length of the rainy season. An inter-comparison between the open-path and a closed-path infrared sensor indicated no systematic errors related to the instrumentation. An uncertainty analysis of long-term NEE budgets indicated that corrections for air density fluctuations were the largest error source (11.3% out of 24.3% uncertainty). Soil organic carbon data indicated a substantial increase in the soil organic carbon pool for the uppermost .20 m. These findings have large implications for the perception of the carbon sink/source of Sahelian ecosystems and its response to climate change.

Assessing drivers of vegetation changes in drylands from time series of earth observation data

This chapter summarizes methods of inferring information about drivers of global dryland vegetation changes observed from remote sensing time series data covering from the 1980s until present time. Earth observation (EO) based time series of vegetation metrics, sea surface temperature (SST) (both from the AVHRR (Advanced Very High Resolution Radiometer) series of instruments) and precipitation data (blended satellite/rain gauge) are used for determining the mechanisms of observed changes. EO-based methods to better distinguish between climate and human induced (land use) vegetation changes are reviewed. The techniques presented include trend analysis based on the Rain-Use Efficiency (RUE) and the Residual Trend Analysis (RESTREND) and the methodological challenges related to the use of these. Finally, teleconnections between global sea surface temperature (SST) anomalies and dryland vegetation productivity are illustrated and the associated predictive capabilities are discussed.

Assessment of Vegetation Trends in Drylands from Time Series of Earth Observation Data

This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when performing temporal trend analysis is introduced and a method for image downscaling (Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)) is presented.