## 3 Results and Discussion
### Annual Changes in GPP
(2017) also suggest that mangrove carbon production is more rapid than other estuarine and marine primary producers. On the other hand, Landsat data are some of the primary sources for identifying areas of forests in space and time with good accuracy ([PERSON] et al., 2017), including in urban areas ([PERSON] et al., 2017) and mangroves ([PERSON] et al., 2017). However, this study still has some uncertainties. The land use data used in this study do not match the year, especially for GPP analysis in 1995, which may cause a mismatch in extracting the estimated GPP. This mismatch will lead to an underestimation of GPP values in settlements and an overestimation in agriculture and grass areas because most of the settlement areas come from agriculture and grass areas. From 1992 to 1995, Denpasar had an average population growth rate of 2.85% per year, and foreign tourist visits to Bali grew an average of 13.47% per year in the same period. One consequence is the increasing number of multi-functional buildings for tourism services and the number of residences needed to accommodate the population growth. However, the four-year difference may not have a significant impact on a study that had a long span period. In addition, the use of 30-m Landsat data can cause errors in detecting vegetation in heterogeneous settlement areas. As shown by [PERSON] et al. (2016), different spatial resolutions in the satellite data used for analyses of GPP in urban areas will lead to different averages for settlement areas, and more detailed spatial resolutions will give larger average values. Increased pixel sizes (or decreased spatial resolution) result in the loss of image detail ([PERSON] et al., 2016). Satellite data with high spatial resolutions may be able to reduce the difficulties with remotely sensed data from coarse-resolution satellites ([PERSON] et al., 2016). Finally, this is the first study that used the VPM method with medium-spatial-resolution satellite imagery to calculate GPP in urban areas; therefore, the results are questionable. Previously, [PERSON] et al. (2017) tried to compare GPP-based VPM estimates (using MODIS data) and MODIS GPP data products (MOD17A2) with solar-induced chlorophyll fluorescence (SIF) over the most populous megacity area with better results compared to those produced with the MOD17A2 product. On the other hand, the total annual GPP estimated using the VPM in the current study was not very different from the GPP estimate produced by other LUE models or from the MODIS GPP product, as presented by [PERSON] et al. (2016) in the same location. This study can be used as an initial source of information related to land use changes and their impact on terrestrial carbon uptake by vegetation. However, urban areas are highly heterogeneous landscapes that have different or even opposing effects on overall urban vegetation productivity ([PERSON] et al., 2016). Urban areas also have complex climate systems that are affected by complex socio-ecological systems ([PERSON] et al., 2016) and the urban-rural proportion as related to urban heat islands ([PERSON] et al., 2016), which directly and indirectly affect the calculation of light-use efficiency. Therefore, a validation of the estimated GPP results of the VPM method with eddy flux towers needs to be performed to advance our quantitative understanding of the capability of this method to contribute to analyses of vegetation carbon uptake in urban areas.