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

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 (NPPA) 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 20m SPOT-4 multi-spectral (XS) image. We then predicted NPPA 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 NPPA. 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 NPPA derived from uncorrected 1 km VEGETATION pixels was significantly higher than estimates using adjusted NDVI values; all three methods reduced the predicted NPPA. In areas of the landscape with a large degree of forest fragmentation (such as forest boundaries) predictions of NPPA indicate that the fragmentation effect has implications for spatially extensive estimates of carbon uptake by forests.