## 2 Study Area, Data Sources, and Methodology
### Methodology
#### 2.3.2 Carbon Uptake Potential by Segmenting Urban Environments and Neighborhood-Based Analysis  
The carbon uptake potential from optimal LMPs was assessed based on the segmentation of homogeneous urban environments and neighborhood-based analysis. First, the effect on NPP from the environmental factors was isolated through region segmentation, resulting in non-overlapped homogeneous patches with identical conditions of the corresponding environments. The internal variation of NPP across locations in the patches was then evaluated and the potential of carbon uptake was computed recursively at location from the statistics of the NPP distribution within the patches by a neighborhood-based analysis. The processes are detailed as follows. The impact from the climate factors, including precipitation, temperature, and solar radiation, has been isolated in the models, i.e., CASA and 3-PGS, in the spatial variations of the output NPP. Next, the impact on NPP from different landforms, soil properties, and vegetation cover must be isolated across locations, leaving the impact from LMPs only. In this regard, a region segmentation operation was performed by overlaying landforms (L), vegetation types (V), and soil types (S) to derive homogeneous patches known as LVS labeled zones so that all locations (pixels) in an LVS zone are labeled by the same LVS#, or properties of the landforms, vegetation cover, and soil type (Figure 2). For the pixels (locations) within zones labeled by the same LVS#, the impact from the LVS factors on the internal NPP variation should be insignificant. Thus, the NPP variation in an LVS zone, if any, should be attributed only to differed LMPs across the locations in LVS zones. Given a location i with relatively low vegetation NPP (NPPi) in an LVS zone, the carbon uptake potential or the extra carbon that vegetation can sequester after implementation of optimal LMPs is computed as the gap from NPPi to a reasonably higher target NPP level (NPPtarget,i) observed at all identically LVS labeled locations in its neighborhood, in the form of:  
\[\mathrm{NPP}_{\mathrm{gap,\,i}}=\mathrm{NPP}_{\mathrm{Target,i}}-\mathrm{NPP }_{\mathrm{i}} \tag{6}\]  
where NPPgap,i is the extra carbon at location i that vegetation can sequester through the implementation of optimal LMPs. NPPTarget,i can be decided either simply by looking for a high NPP at other locations (e.g., the local maximum NPP) or computed statistically from the NPP distribution observed at all other locations in the same LVS labeled zones (e.g., the top 10% NPP level for all locations). The maximum NPP flux in segmented zones labeled by a unique LVS# may theoretically serve as the NPPTarget,i for other locations in the zones. However, this configuration means that the optimal LMPs are too strictly defined to be implemented because only a single candidate is available in the optimal LMPs list, leaving no alternative choices in the selection of the optimal LMPs. It is more reasonable to compute NPPTarget,i by setting a flexible standard based on the context of NPP in the LVS zones. Due to the complexity in urban ecosystems, here we consider two parameters to compute the NPPTarget,i. The first one is the size of the neighborhood, or a radius defined by the distance starting from the location i. A larger distance means more locations in the neighborhood to be searched to derive the NPPTarget. The second parameter is a reference percentage (RP), which is a percentile threshold to derive the NPPTarget,i from the NPP distribution of all the locations in the LVS zones. A high RP means a higher NPPTarget,i and greater carbon uptake potential but, at the same time, fewer LMPs candidates for practical implementation. Instead of targeting the maximum NPP (RP = 100%), setting RP = 90% is more robust to data noises in the segmented patches and the modeled NPP because this setting cuts off 10% of the top NPP values to avoid the impact on NPP\({}_{\text{Target,i}}\) from abnormal data in NPP.