Kasturi Devi Kanniah

Control of atmospheric particles on diffuse radiation and terrestrial plant productivity: A review

Terrestrial plant productivity tends to increase under increasing but non-saturating photosynthetically active solar radiation when water, temperature and nutrients are not limiting. However, studies have shown that photosynthesis can also be higher under enhanced diffuse light despite a decrease in total irradiance. Clouds and atmospheric aerosols are two important variables that determine the total and proportion of diffuse light reaching the surface and thereby the rate of photosynthesis and carbon accumulation in plants. In addition to these factors, the response of plants to diffuse radiation is also dependant on plant characteristics such as functional types, leaf physiology, leaf area, leaf inclination, canopy structure and shape (i.e. clumping). Local environmental conditions (i.e. temperature, soil moisture, vapour pressure deficit, etc.) then modulate these plant responses. Changes in solar radiation as a consequence of clouds and aerosols thus can modify the carbon balance of terrestrial ecosystems. Therefore, understanding the role of solar radiation in terrestrial carbon processes has become one of the goals in terrestrial carbon cycle studies. It can help to identify the control and mechanisms of carbon processes and determines the geographical and temporal distribution of the major pools and fluxes in the global carbon cycle. Here we review the role of clouds and aerosols in partitioning solar radiation and their interactions with carbon processes of terrestrial plants. We also focus our review on vegetation characteristics that control the impact of radiation partitioning on vegetation carbon processes and the role of modelling approach to study this impact. We identify gaps in this field of research and further propose recommendations to bridge the gap.

Special- savanna patterns of energy and carbon integrated across the landscape

Savannas are highly significant global ecosystems that consist of a mix of trees and grasses and that are highly spatially varied in their physical structure, species composition, and physiological function (i.e., leaf area and function, stem density, albedo, and roughness). Variability in ecosystem characteristics alters biophysical and biogeochemical processes that can affect regional to global circulation patterns, which are not well characterized by land surface models. We initiated a multidisciplinary field campaign called Savanna Patterns of Energy and Carbon Integrated across the Landscape (SPECIAL) during the dry season in Australian savannas to understand the spatial patterns and processes of land surface–atmosphere exchanges (radiation, heat, moisture, CO2, and other trace gasses). We utilized a combination of multiscale measurements including fixed flux towers, aircraft-based flux transects, aircraft boundary layer budgets, and satellite remote sensing to quantify the spatial variability across a continental-scale rainfall gradient (transect). We found that the structure of vegetation changed along the transect in response to declining average rainfall. Tree basal area decreased from 9.6 m2 ha−1 in the coastal woodland savanna (annual rainfall 1,714 mm yr−1) to 0 m2 ha−1 at the grassland site (annual rainfall 535 mm yr−1), with dry-season green leaf area index (LAI) ranging from 1.04 to 0, respectively. Leaf-level measurements showed that photosynthetic properties were similar along the transect. Flux tower measurements showed that latent heat fluxes (LEs) decreased from north to south with resultant changes in the Bowen ratios (H/LE) from a minimum of 1.7 to a maximum of 15.8, respectively. Gross primary productivity, net carbon dioxide exchange, and LE showed similar declines along the transect and were well correlated with canopy LAI, and fluxes were more closely coupled to structure than floristic change.

Fire in Australian savannas: from leaf to landscape

Savanna ecosystems comprise 22% of the global terrestrial surface and 25% of Australia (almost 1.9 million km2) and provide significant ecosystem services through carbon and water cycles and the maintenance of biodiversity. The current structure, composition and distribution of Australian savannas have coevolved with fire, yet remain driven by the dynamic constraints of their bioclimatic niche. Fire in Australian savannas influences both the biophysical and biogeochemical processes at multiple scales from leaf to landscape. Here, we present the latest emission estimates from Australian savanna biomass burning and their contribution to global greenhouse gas budgets. We then review our understanding of the impacts of fire on ecosystem function and local surface water and heat balances, which in turn influence regional climate. We show how savanna fires are coupled to the global climate through the carbon cycle and fire regimes. We present new research that climate change is likely to alter the structure and function of savannas through shifts in moisture availability and increases in atmospheric carbon dioxide, in turn altering fire regimes with further feedbacks to climate. We explore opportunities to reduce net greenhouse gas emissions from savanna ecosystems through changes in savanna fire management.

The comparative role of key environmental factors in determining savanna productivity and carbon fluxes: A review, with special reference to northern Australia

Terrestrial ecosystems are highly responsive to their local environments and, as such, the rate of carbon uptake both in shorter and longer timescales and different spatial scales depends on local environmental drivers. For savannas, the key environmental drivers controlling vegetation productivity are water and nutrient availability, vapour pressure deficit (VPD), solar radiation and fire. Changes in these environmental factors can modify the carbon balance of these ecosystems. Therefore, understanding the environmental drivers responsible for the patterns (temporal and spatial) and processes (photosynthesis and respiration) has become a central goal in terrestrial carbon cycle studies. Here we have reviewed the various environmental controls on the spatial and temporal patterns on savanna carbon fluxes in northern Australia. Such studies are critical in predicting the impacts of future climate change on savanna productivity and carbon storage.

Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.

Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia

This article demonstrates some techniques for studying the age of oil palm trees (Elaeis guineensis Jacq.) using the Disaster Monitoring Constellation 2 from the UK (UK-DMC 2) and Advanced Land Observing Satellite phased array L-band synthetic aperture radar (ALOS PALSAR) remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely (1) radiance, vegetation indices, and fraction of shadow; (2) texture measurement; (3) classifications, namely Iterative Self-Organizing Data Analysis Technique (ISODATA) classification, maximum-likelihood classification (MLC), and random forest (RF) classification; (4) in terms of ALOS PALSAR data, the correlation of polarizations (i.e. horizontal transmitting and horizontal receiving (termed HH polarization) and horizontal transmitting and vertical receiving (termed HV polarization)) and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 (near-infrared) of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix (GLCM) demonstrated strong correlation of determination (R 2 = 0.76–0.80) with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly (R 2 = 0.49) with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% (MLC method) to 52.9%. This study concluded that texture measurement (GLCM mean) and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.

Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km2 the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.

Per-pixel and sub-pixel classifications of high-resolution satellite data for mangrove species mapping

High spatial resolution sensors such as IKONOS and QuickBird, are expected to classify mangrove species more accurately than coarse spatial resolution satellite images. Conventional per-pixel classification techniques could not improve the classification accuracy when such high-resolution images are applied. Such failure has encouraged the invention of more sophisticated and deterministic techniques i.e. subpixel classifications. In this study, the mangrove forest at Sungai Belungkor, Johor, Malaysia was classified using IKONOS data. Two classification approaches were applied, namely per-pixel and sub-pixel techniques. The conventional per-pixel classifiers used in this study were Maximum Likelihood (ML), Minimum Distance to Mean (MDM) and Contextual Logical Channel (CLC) while the Linear Mixture Model (LMM) was selected as the sub-pixel classification approach. The classification results revealed that the CLC classification with a contrast texture measure at window size 21 x 21 yielded the highest accuracy (82%) in comparison to the ML (68%) or MDM (64%). The spatial distribution of the classified mangrove species and classes coincided with the common mangrove zones in Malaysia. For the results of the LMM, the fraction of pixels measured from the satellite imagery and observed in the field gave a good correlation with an R2 value of 0.83 for Bakau minyak, a moderate correlation with an R2 of approximately 0.71 for Bakau kurap and an R2 of 0.75 for the ‘Others’ type of mangrove species. An error image was also created to compare the best fitting spectrum produced by the inversion of the LMM with the original observed spectrum, where the maximum RMS error was only 5%.

A review of remote sensing based productivity models and their suitability for studying oil palm productivity in tropical regions

Oil palm (Elaeis guineensis Jacq.) cultivation has been expanding and has become one of the fastest developing agricultural crops in tropical regions. Therefore, it is critical to understand the carbon balance and dynamics within oil palm estates to determine its role in the global carbon cycle. Estimating oil palm productivity on a large scale is most feasible with remote sensing based models. Thus, the objective of this paper is to review existing remote sensing based models (i.e. CASA, GLO-PEM, VPM, C-Fix, TURC, EC-LUE, VI, TG, 3-PGS and MOD17) that use light use efficiency (LUE) logic, and subsequently to evaluate the suitability of these models for estimating oil palm productivity. This paper also highlights the limitation of current remote sensing based models for estimating oil palm productivity. From the review of existing literature, it is clear that the existing remote sensing based models need to be modified in terms of meteorological inputs, maximum LUE and environmental constraints in order to improve the estimation of oil palm productivity.

Response of savanna gross primary productivity to interannual variability in rainfall: Results of a remote sensing based light use efficiency model

Studying the temporal pattern of savanna gross primary productivity (GPP) is essential for predicting the response of the biome to global environmental changes. In this study, MODIS satellite data coupled with eddy covariance based flux measurements were used to estimate GPP using a remote sensing based light use efficiency model across a significant rainfall gradient in the Northern Territory (NT) region of Australia. Closed forest that occurred in wet and often fireproof environments assimilated (GPP) 4–6 times more carbon than grasslands and Acacia woodlands that grow in arid environments (<600 mm annual rainfall). However, due to their small spatial extent, closed forests contributed <0.5% of the regional budget compared to savanna woodlands (86%) and grasslands (32%). Annual rainfall was found to exert a significant influence on GPP for different vegetation types except for closed forest which was less sensitive to above-average rainfall. Interannual variability in GPP showed that arid ecosystems had a higher variation (>20%) compared to woodlands and forest (∼5%). This variation in GPP was correlated with that of rainfall (R2 = 0.88, p<0.05). Analysis of the impact of wettest and driest years on GPP showed a strong positive correlation between the magnitude of the relative maxima in rainfall and maxima in GPP (R2 = 0.89, p<0.05). In contrast, the relative rainfall minima exhibited an insignificant relationship with relative GPP minima (R2 = 0.45, p = 0.07). These findings provide valuable information on the carbon uptake across the savanna biome and show the sensitivity of different vegetation systems to rainfall, a variable that may change in quantity and variability with projected climate change. Such data also show regions of high levels of carbon that could be linked with savanna management to protect the resources in the Australian savannas.