Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery

Abstract

Conversion of native forests to agriculture and urban land leads to fragmentation of forested landscapes with significant consequences for habitat conservation and forest productivity. When quantifying land-cover patterns from airborne or spaceborne sensors, the interconnectedness of fragmented landscapes may vary depending on the spatial resolution of the sensor and the extent at which the landscape is being observed. This scale dependence can significantly affect calculation of remote sensing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and its subsequent use to predict biophysical parameters such as the fraction of photosynthetically active radiation intercepted by forest canopies (fPAR). This means that simulated above-ground net primary productivity (NPP_A) using canopy radiation interception models such as 3-PG (Physiological Principles for Predicting Growth), coupled with remote sensing observations, can yield different results in fragmented landscapes depending on the spatial resolution of the remotely sensed data.We compared the amount of forest fragmentation in 1 km SPOT-4 VEGETATION pixels using a simultaneously acquired 20 m SPOT-4 multispectral (XS) image. We then predicted NPP_A for New Zealand native forest ecosystems using the 3-PG model with satellite-derived estimates of the fPAR obtained from the SPOT-4 VEGETATION sensor, using NDVI values with and without correction for fragmentation. We examined three methods to correct for sub-pixel fragmentation effects on NPP_A. These included: (1) a simple conversion between the broad 1 km scale NDVI values and the XS NDVI values; (2) utilization of contextural information from XS NDVI pixels to derive a single coefficient to adjust the 1 km NDVI values; and (3) calculation of the degree of fragmentation within each VEGETATION 1 km pixel and reduce NDVI by an empirically derived amount based on the proportional areal coverage of forest in each pixel. Our results indicate that predicted NPP_A derived from uncorrected 1 km VEGETATION pixels was significantly higher than estimates using adjusted NDVI values; all three methods reduced the predicted NPP_A. In areas of the landscape with a large degree of forest fragmentation (such as forest boundaries) predictions of NPP_A indicate that the fragmentation effect has implications for spatially extensive estimates of carbon uptake by forests.

Acknowledgments

This research was undertaken at Landcare Research New Zealand, and supported in part by the New Zealand Ministry for the Environment and the Foundation for Research, Science, and Technology (CO9807). We thank Dr James Shepherd for the pre-processed SPOT-4 imagery used in this project. Dr Brendan Mackey and Mr Andrew Loughead provided valuable comments on the draft manuscript. Additional support was provided by the Department of Biology, Baylor University, USA and The Woods Hole Research Center, USA. We acknowledge Dr Joe Landsberg (CSIRO Land and Water) and Prof. Richard Waring (Oregon State University) who developed the original 3-PG framework and thank them for their advice and valuable discussions on the use and application of the model. The paper also benefited from comments by the anonymous reviewers.

Additional information on the 3-PG model and free code is available at: http://www.ffp.csiro.au/nfm/mdp/

Notes

^§Present address: Baylor University, Department of Biology, P.O. Box 97388, Waco, Texas 76798–7388, USA.

^¶Present address: Woods Hole Research Center, P.O. Box 296, Woods Hole, Massachusetts 02543, USA.

Additional information

Notes on contributors

J. D. White§

§Present address: Baylor University, Department of Biology, P.O. Box 97388, Waco, Texas 76798–7388, USA.

N. A. Scott¶

¶Present address: Woods Hole Research Center, P.O. Box 296, Woods Hole, Massachusetts 02543, USA.