This section presents the geospatial data used and the respective geospatial analysis approach. All geospatial calculations were performed with ArcGIS Pro. The study area (Fig. 1) is located in the city of Dortmund, North Rhine-Westphalia, Germany. It includes all relevant land cover/land use types and is representative for the region.
Land Use/Land Cover Dataset FNK
The first data set in this study is the land cover/land use mapping FNK created in 2017 by the Regionalverband Ruhr (RVR 2017). It is a very detailed map product that needs to be simplified for the purpose of this study and to comply with the vegetation cover. Originally, this product consists of 151 individual categories, from which 57 are meaningful for this study. Those are reclassified to seven main classes for this investigation (Table 1), as lots of them are only different in usage rather than surface appearance. For different administrative reasons, the FNK does not cover the whole study area. Therefore, maps that are based on this data set have a different extent than those of other geospatial data not affected by administrative border lines.
Table 1 Land cover/land use class reduction for this study (base data from RVR 2017) Sentinel-2 Imagery
Further, freely available cloud-free high resolution Sentinel-2 satellite images (ESA 2020) (see Table 2) are used to investigate the study area during the vegetation period of 2019. From all 12 available spectral bands, only the four bands with 10 m geometric resolution are used. In the authors opinion, bands with coarser resolutions are not suitable for urban environments.
Table 2 Sentinel-2 satellite images (ESA 2020) used for the vegetation period April 1st to August 31st 2019 From the satellite images, the NDVI is calculated to monitor the urban vegetation cover as follows:
$${\text{NDVI}} = \left( {{\text{nIR}}{-}{\text{RED}}} \right)/\left( {{\text{nIR}} + {\text{RED}}} \right).$$
The resulting NDVI values appear in the range of − 1 to + 1. Values below or close to 0 are not related to healthy green vegetation, rather to water, bare soil or abiotic urban surfaces like roofs and road materials. The higher the NDVI value, the more it is related to vegetation cover and high vitality.
During the vegetation period, the reflectance characteristics of vegetation varies among trees (forests, parks), bushes, meadows or agricultural fields with different crops, due to varying cultivation techniques (as well as cutting cycles) or phenology reasons. Other surfaces like roads or buildings are rather invariant over time. This is illustrated for a few typical locations in Fig. 2.
The five satellite images are displayed in Fig. 3 in false colors. They clearly reveal seasonal differences between the different land cover/land use categories: the false colors pronounce vegetation in red. Seasonal effects are also evident in the NDVI-images of the respective dates (Fig. 4). Areas with low NDVI could suffer from little or no vegetation and could be areas of environmental burdens like heat vulnerability during summer months. On the contrary, a high degree of tall vegetation (e.g. trees) generates cooling effects of the neighborhood in summer and therefore leads to healthier conditions of life in this particular area.
Digital Terrain Model
To determine object heights, laser scan data of 2018 (Land NRW 2020) with 1 m raster cells are used. The last pulse based digital terrain model (DTM) is subtracted from the first pulse based digital surface model (DSM) to receive the object height of trees, buildings and other rather vertical objects in a digital object height model (DOHM) that is presented in Fig. 6. For consistency reasons, the 1 m raster cells are resampled to 10 m raster cells using nearest neighbor to match the 10 m NDVI raster cells (Fig. 5).
Calculation of Different Mean NDVIs
Monotemporal NDVI values like in Fig. 4 offer further aggregation possibilities. Besides the calculation of pixel-based NDVI values per image acquisition date or the calculation of NDVI differences between adjacent image dates, further unconventional NDVI calculations help to gain more insight into vegetation dynamics etc.
Among such calculations is the aggregation to spatial, temporal or spatio-temporal mean or maximum NDVI values to characterize and spatially differentiate greenspaces representing different land cover types as outlined in Table 3.
Table 3 Overview of different NDVI calculations In this study, the first NDVI analysis (type A) is based on mean NDVI values over time for all pixel locations. These temporal mean NDVIs per pixel allow insights into seasonality aspects, as these calculated mean values give an overview of the NDVI level within a season. Rather low temporal mean NDVI´s indicate pixels with sparse or no vegetation during most of the season. Rather high values indicate a high amount of photosynthetic active vegetation during most of the season. Medium values could represent fields with cultivated intensively green periods and harvested situations characterized by rather low NDVI values.
Another reasonable approach is to average mean NDVI values spatially for each land cover/land use polygon. The authors call this a spatio-temporal mean NDVI per FNK polygon (type B), which allows insights into an annual or seasonal average NDVI per polygon. This appears to be more meaningful for individual land cover/land use categories than single pixel based calculations. Moos (2020) does so for the vegetation period for the city of Dortmund in Germany. Due to his intention to study the mean NDVI for a complete city, he used a coarse grid (cell size 100 m × 100 m) and combined the temporal mean NDVI calculation with the spatial mean NDVI calculation. Disadvantageous for the coarse grid cells is, that land surfaces with no or little vegetation are included in the calculation and consequently lower the resulting mean NDVI value. To overcome this disadvantage, the authors calculate the spatial–temporal mean on a field or parcel basis (FNK polygon) with homogeneous land cover/land use within each polygon. This gives a much more realistic and sophisticated representation of greenness in the urban neighborhood.
In addition to temporal mean NDVI calculations, the temporal maximum NDVI per pixel (type C) for the period of observation (April–August 2019) is extracted. Aside from identifying the maximum value per pixel, the authors also extract the month associated with that maximum value. This is the month in which greenness is most intense.
In a further step, the most frequent (statistical modus) of these date/month-related temporal maximum NDVI (type C) is calculated on a field or parcel basis to identify the month of the spatio-temporal maximum NDVI per polygon (type D) of each land cover/land use class (field boundary). This helps to understand the temporal variability of slightly generalized maximum NDVI values. Having those calculations with maximum NDVI and the modus of the spatio-temporal maximum NDVI per field boundary, one can identify the maximum intensity value of the NDVI (or greenness) per vegetation period and isolate the month with the maximum greenness for the respective FNK polygon.
Combination of NDVI and Vegetation Height
For health-related studies, the physical appearance of urban green could be of great importance. For that kind of investigation, one should differentiate different heights of vegetation as human beings perceive vegetation depending on their size. Green meadows probably have another individual perception than tall green forests. Since NDVI does not necessarily account for vegetation height, it makes sense to compare NDVI to plant height and create a limited number of meaningful height classes for studies on urban greenspace.
Based on the DOHM, the individual vegetation height is reduced into three meaningful categories relevant to the human perception of vegetation: smaller than 2 m, 2–5 m and above 5 m height, that easily correspond with the land cover classes. This height categorization is intended to distinguish vegetation categories with typical physical height appearance.
In this experimental study, the combination of vegetation height data and NDVI data in a bivariate choropleth map (Götze and van den Berg 2017; Moos 2020) is applied to gain additional information from the used data sets. Moos (2020) applies three NDVI classes for the characterization of green urban infrastructure: < 0.3; 0.3–0.6 and > 0.6. The higher the value, the better the amount of green infrastructure and its condition. This classification scheme with three categories is adopted here according to its practicability and the easy readability of the resulting map.
In addition to the bivariate choropleth map, a correlation between the land cover/land use categories and the nine object height/NDVI-classes could be meaningful. For this analysis, the bivariate choropleth map is calculated on a raster cell basis instead of parcel polygons. Afterwards, a simple frequency analysis of each of the nine classes reveals the class composition.