Booker Ogutu

An algorithm to derive the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (FAPARps) from eddy covariance flux tower data

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key vegetation biophysical variable in most production efficiency models (PEMs). Operational FAPAR products derived from satellite data do not distinguish between the fraction of photosynthetically active radiation (PAR) absorbed by nonphotosynthetic and photosynthetic components of vegetation canopy, which would result in errors in representation of the exact absorbed PAR utilized in photosynthesis. The possibility of deriving only the fraction of PAR absorbed by photosynthetic elements of the canopy (i.e. FAPAR(ps) ) was investigated. The approach adopted involved inversion of net ecosystem exchange data from eddy covariance measurements to calculate FAPAR(ps) . The derived FAPAR(ps) was then related to three vegetation indices (i.e. Normalized Difference Vegetation Index (NDVI), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Enhanced Vegetation Index (EVI)) in an attempt to determine their potential as surrogates for FAPAR(ps) . Finally, the FAPAR(ps) was evaluated against two operational satellite data-derived FAPAR products (i.e. MODIS and CYCLOPES products). The maximum FAPAR(ps) from the inversion approach ranged between 0.6 and 0.8. The inversion approach also predicted site-specific Q₁₀-modelled daytime respiration successfully (R² > 0.8). The vegetation indices were positively correlated (R² = 0.67-0.88) to the FAPAR(ps). Finally, the two operational FAPAR products overestimated the FAPAR(ps). This was attributed to the two products deriving FAPAR for the whole canopy rather than for only photosynthetic elements in the canopy.

Methods to Quantify Regional Differences in Land Cover Change

This paper describes and illustrates methods for quantifying regional differences in land use/land cover changes. A series of approaches are used to analyse differences in land cover change from data held in change matrices. These are contingency tables and are commonly used in remote sensing to describe the spatial coincidence of land cover recorded over two time periods. Comparative analyses of regional change are developed using odds ratios to analyse data in two regions. These approaches are extended using generalised linear models to analyse data for three or more regions. A generalised Poisson regression model is used to generate a comparative index of change based on differences in change likelihoods. Mosaic plots are used to provide a visual representation of statistically surprising land use losses and gains. The methods are explored using a hypothetical but tractable dataset and then applied to a national case study of coastal land use changes over 50 years conducted for the National Trust. The suitability of the different approaches to different types of problem and the potential for their application to land cover accuracy measures are briefly discussed.

Using vegetation spectral indices to detect oil pollution in the Niger Delta

Vegetation health and vigour may be affected by oil leakage or pollution. This effect can alter a plant’s behaviour and may be used as evidence for detecting oil pollution in the environment. Satellite remote sensing has been shown to be an effective tool and approach to detect and monitor vegetation health and status in polluted areas. Previous research has used vegetation indices derived from remotely sensed satellite data to monitor vegetation health. This study investigated the potential for using broadband multispectral vegetation indices to detect impacts of oil pollution on vegetation conditions. Twenty indices were explored and evaluated in this study. The indices use data acquired at the visible, near infrared and shortwave infrared wavelengths. Comparative index values from the 37 oil polluted and non-polluted (control) sites show that 12 Broadband multispectral vegetation indices (BMVIs) indicated significant differences (p-value < 0.05) between pre- and post-spill observations. The 12 BMVI values at the polluted sites before and after the spill are significantly different with the ones obtained on the spill event date. The result at the non-polluted (control) sites shows that 11 of the 20 BMVI values did not indicate significant change and remained statistically invariant before and after the spill date (p-value > 0.05). Therefore, it can be stated that, in this study, oil spills seem to result in biophysical and biochemical alteration of the vegetation, leading to changes in reflectance signature detected by these indices. Five spectral indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), adjusted resistant vegetation index (ARVI2), green near infrared (G/NIR) and green shortwave infrared (G/SWIR)) were found to be consistently sensitive to the effects of oil pollution on vegetation and hence could be used to map and monitor oil pollution in vegetated areas.

Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK

Leaf area index (LAI) is a key biophysical variable influencing land surface fluxes. Different algorithms have been developed to estimate LAI from remote sensing data. This prompts the need for an evaluation of their comparability and performance. We present an evaluation of the comparability of four products (i.e., MODIS (MOD15A2), NN-MERIS, CYCLOPES, and GLOBCARBON) and their performance against in situ LAI for an entire growing season in a broadleaf deciduous forest. All the LAI products detected the phenological trend of this biome reasonably accurately, albeit with differences in absolute values. The MODIS LAI was higher than the in situ LAI throughout the growing season whereas the GLOBCARBON LAI was higher in the summer months. The NN-MERIS was closest to the in situ measurements whereas the CYCLOPES product was lower than the in situ measurements. The NN-MERIS and CYCLOPES LAI were closely matched (RMSE = 0.45), whereas MODIS and CYCLOPES LAI were the most divergent (RMSE = 1.57). All the algorithms were significantly different (p < 0.05) indicating a need for more efforts to harmonize these algorithms. Finally, the spatial consistency between the NN-MERIS LAI and in situ LAI revealed a season dependency trend. Better spatial agreement was observed during the summer season as opposed to early spring and autumn seasons.

Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.

Evaluation of the influence of two operational fraction of absorbed photosynthetically active radiation FAPAR products on terrestrial ecosystem productivity modelling

The fraction of photosynthetically active radiation absorbed by vegetation (FAPAR) represents the available light energy for plant productivity and is the key variable influencing photosynthesis, transpiration, and energy balance in most terrestrial vegetation productivity models. With availability of earth observation data from different satellite sensors increasing, a number of FAPAR products are being generated. Several studies have investigated the differences between these products. However, very few studies have investigated how the differences between these products influence the output from ecosystem productivity models that utilise them. This study evaluated the influence of two operational FAPAR products (i.e. the MODIS and CYCLOPES FAPAR products) on the terrestrial vegetation primary productivity predicted by the Carnegie–CASA model across various biomes in the USA. The GPP predicted by the Carnegie–CASA model was compared to GPP measurements from various flux tower sites representing five biomes (i.e. croplands, broadleaf deciduous forests, grassland, needle-leaf evergreen forests, and savanna woodland). With the exception of cropland sites, the two FAPAR products resulted in GPP predictions which were higher than the in situ GPP measurements for the evaluated biomes. However, the CYCLOPES FAPAR product resulted in GPP outputs which were closer (lower RMSE values) to the in situ measurements than the MODIS FAPAR product. The two FAPAR products do not account for the FAPAR absorbed by non-photosynthetic elements of the canopy, which may lead to overestimation of the FAPAR that is actually used in photosynthesis. This could explain the higher GPP values derived using these products when compared to the in situ GPP measurements.

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

ABSTRACT The aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.

Detection of oil pollution along the pipeline routes in tropical ecosystem from multi-spectral data

The study was conducted in an oil producing environment dominated by mangrove and swamp vegetation in Niger Delta, Nigeria. Ancillary data including oil pipeline map and GPS of spill points were used in selecting sample sites to identify and detect polluted locations. A number of polluted and non-polluted sites were selected and vegetation spectral reflectance and indices for these sample sites were extracted from TM data of January and December 1986. A statistical T-test was used to test for significant differences between vegetation spectral reflectance and indices from polluted and non-polluted sites. The initial results from the analysis of spectral reflectance between polluted and non-polluted did not show any significant difference in all the six spectral bands with p-value <0.005. The results from analysis of various vegetation indices some did not show any significance differences between the polluted and non-polluted sites (e.g. the SRI, SAVI and EVI2). Other VIs (NDVI, MSAVI2 and ARVI2) showed significant differences between the polluted and non-polluted sites. From these preliminary results we can conclude that pollution from oil spills may result to the changes in leaf biochemistry of the Mangroves in the Niger Delta which are detectable from remote sensing data. Future work will focus on undertaking further temporal analysis of additional spill sites to determine what quantity of spilt oil arises in spectral changes of vegetation.

Remote sensing for detection and monitoring of vegetation affected by oil spills

ABSTRACT This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR – p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests.

Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPARps) and (ii) using direct quantum yield by classifying the vegetation into C3 or C4 classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm-2day-1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm-2day-1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr-1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.