Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna

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Highlights

  • The geographic information system (GIS) and digital elevation model (DEM) modelled shortwave radiation (SWR) provides an efficient solution to estimating “continuous field” SWR over large scales for net primary productivity (NPP) modelling.

  • The modified MODNPP model can successfully be used to model NPP in African Savanna landscapes.

  • NPP results of this study fall within the range established by a range of NPP studies in African Savannas found in refereed literature.

  • The modified MODNPP model estimates NPP better than the established Lieth NPP.

Abstract

We tested whether and to what extent a remote sensing net primary productivity (NPP) estimation model based on the moderate resolution imaging spectroradiometer (MODIS) data and GIS modelled shortwave radiation can be used to estimate NPP in a Southern African Savanna landscape. To accomplish this, we compared the results of our model with results obtained using an established rainfall-NPP regression model developed by Lieth and Whittaker (1975) for the savanna landscape, as well as the dry matter productivity (DMP) remotely sensed model. Results show that our model estimates do not significantly (p > 0.05) differ with results from the Lieth regression model with an R2 of 0.67. In addition, results showed that our remotely sensed NPP correlated significantly (p < 0.05) with the DMP results (Pearson Correlation R = 0.91). These results suggest that we can successfully model African Savanna NPP using MODIS remotely sensed data in combination with GIS modelled clear sky and topographically based shortwave radiation (SWR) flux.

Introduction

In studies of ecosystem processes and environmental management, reliable net primary productivity (NPP) measurements are crucial as NPP represents an important energy flux in ecosystems. Traditionally, NPP has been measured using biometric measurements, i.e., sample surveys and field measurements (Tao et al., 2003). Although these traditional field-based measurements have been used successfully with accurate NPP output for small scale observations (Ogawa, 1977, Li et al., 2005), they are often time consuming and laborious (Lu, 2006). The methods are also hard to extend to the estimation of NPP on large scales because of the sparse measurements network. Also the need to fell sample trees at the target research site may adversely affect the site, for example, loss of habitat, biodiversity and carbon sequestration potential (Wang et al., 2007). Therefore, it is necessary to apply alternative methods of NPP estimation to replace or supplement traditional approaches in collecting ecological data.

The development of remote sensing has by far enhanced the ability to study and understand ecosystems with improved accuracy (Lu, 2006). Remote sensing provides an invaluable opportunity to improving the estimation of NPP at landscape and regional scales in a cost effective, efficient and accurate approach (Running et al., 1999, Running, 2000) at high temporal and spatial scales (Myneni et al., 1997, Myneni et al., 1998, Tucker et al., 2001, Zhou et al., 2001, Lu, 2006). As a result, various NPP estimation models that use remote sensing data have been developed (Goetz et al., 2000). Recently, the moderate resolution imaging spectroradiometer (MODIS) NPP, based on a micrometeorological approach was developed by Rahman et al. (2004) to provide a consistent, continuous estimate of photosynthetic production (Heinsh et al., 2006) hereinafter referred to as the MODNPP model. This model is derived from the Monteith model (Monteith, 1977) which is given by the equation:NPP=LUR×APARwhere, LUE is light use efficiency and APAR is absorbed photosynthetically active radiation by vegetation. However, the model’s limitation has been on its demands for densely measured shortwave radiation (SWR) flux for APAR estimation, as well as LUE values. The LUE and SWR flux measurements have mainly been obtained from coarse resolution satellite-based weather data, making NPP studies difficult in cases where intensively measured LUE and SWR information is required (Turner et al., 2003, Rahman et al., 2004, Running et al., 2004). Scientists have therefore largely depended on calibrated LUE (Rahman et al., 2004) and SWR (Kumar et al., 1997) values whose accuracy is still largely unknown. Thus, the development of efficient and accurate methods for estimating LUE and SWR is critical.

Modifications to the MODNPP model established by Rahman et al. (2004) can however be made. For example, Kumar et al. (1997) used a digital elevation model (DEM) and geographic information system (GIS) radiation model to compute “continuous-field” SWR flux measurements which offer an alternative source of SWR. The availability of DEM data for virtually any location on earth since the launch of space-borne earth observation sensors like ASTER (Stevens et al., 2004) and the Shuttle Radar Topography Mission (SRTM) (Rabus et al., 2003) has enhanced opportunities of using fine to medium scale DEMs in ecological modelling at landscape scale (Ndaimani et al., 2012). In the present study, a considerable modification to the MODNPP model after Rahman et al. (2004) was made. SWR, a key input of NPP estimation in this model, has been estimated using GIS and DEM based SWR model contrary to previous studies which has used SWR measured from weather stations. To the best of our knowledge, we are not aware of any study that has used DEM and GIS modelled SWR to estimate NPP. Hence, testing whether SWR modelled from a DEM in a GIS can successfully be used to estimate NPP is important.

Evaluation of model performance and accuracy assessment of the estimated results are important aspects in the NPP estimation procedure (Lu, 2006). Scientists have often adopted two main methods to evaluate model performances. One is based on the coefficient of determination (R2) that is if the models were developed using multiple regression analysis and the other assesses the root-mean-squared error (RMSE) such as in Muinonen et al. (2001) and Rasib et al. (2007). In general, a high R2 or a low RMSE value often indicates a good fit between the model developed and the sample plot data. However, due to unavailability of field-measured NPP, NPP model results can be compared with outputs of other NPP-models. Lu (2006) noted that previous research on NPP estimation have failed to provide accuracy assessments due to the difficulty in collecting ground reference data or the discrepancy between field measurements and NPP estimation results. Although there are many challenges associated with obtaining ground reference data, it is important to test modelled results with results from other measurement approaches as a way of improving accuracy before the model can be adopted for further research.

In this study we tested whether and to what extent the DEM and GIS modelled SWR can be used in the MODNPP model to estimate NPP in an African Savanna landscape. To achieve this, we tested whether the NPP estimated using the modified model significantly differ with those obtained using an established NPP-Rainfall regression model developed by Lieth and Whittaker (1975). Next, we compared our model results with those found in refereed scientific literature in an African Savanna. Finally we tested whether our modelled NPP was significantly related to dry matter productivity (DMP) results from VITO (http://www.geoland2.eu/). Specifically, we tested the hypothesis that our MODNPP results do not significantly differ from NPP results of other NPP models within the same ecological landscape.

Section snippets

Study site

This study was conducted in the Savanna landscape of the Great Limpopo Transfrontier Conservation Area (GLTFCA) which lies across Zimbabwe, South Africa and Mozambique, between 30.70 °E and 35.00 °E, and 25.50 °S and 20.30 °S. The GLTFCA is a union of the Limpopo National Park (LNP) in Mozambique, Kruger National Park (KNP) in South Africa, Gonarezhou National Park (GNP), Manjinji Pan Sanctuary (MPS) and Malipati Safari Area (MSA) in Zimbabwe, as well as two areas between Kruger and Gonarezhou,

Results

The modelled SWR is significantly (p < 0.05) positively related to SWR measured from weather stations with an R2 of 0.68 (Fig. 3). In addition, a Student t-test analysis showed that measured SWR is not significantly (p > 0.05, N = 24) different from the modelled SWR.

The range of NPP values for the MODNPP and Lieth NPP are almost similar with NPP ranging from 0 to 6 ton C ha−1 year−1 whilst NPP values range up to 30 ton C ha−1 year−1 for the DMP (Fig. 4(a)–(c)).

Table 1 shows the distribution of NPP (ton C ha−1 

Discussion

Our results suggest that our modified MODNPP model can be used to successfully estimate NPP in savanna ecosystems. This fills an important knowledge gap as previous studies on the estimation of savanna NPP have mainly been using biometric measurements (Miller et al., 2004). Most importantly, our results fall within the range established by a range of NPP studies in African Savannas (Table 2).

Specifically, results of the GLTFCA long-term annual MODNPP mean falls within 95% CI of 5.8 ± 1.9 ton C ha−1 

Conclusion

The objective of this study was to test whether and to what extent the modified MODNPP model can successfully be used to model NPP in an African Savanna landscape. Based on the results, we can conclude that the modelled SWR can effectively be used in the MODNPP model in place of calibrated SWR weather station data. We also conclude that the modified MODNPP model can be used successfully to model NPP in African Savanna landscapes. Results of this study imply that GIS modelled SWR is a useful

Acknowledgements

This research was conducted within the RP-PCP Research Platform. We thank the French Embassy in Zimbabwe for their financial support through CIRAD (RP-PCP grant/Project ECO#1). Several persons at the University of Zimbabwe and CIRAD Zimbabwe helped in many ways during this research. Of particular mention are Dr Michel Degarine, Dr Mhosisi Masocha and Fadzai Zengeya for their technical support.

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