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Landforms are natural features of the earth’s surface created by topography - collectively the set of landforms comprises a region’s terrain. A single landform can be described as a combination of topographic position, aspect, slope, and moisture (e.g., moist north-facing toeslope). The distribution of landforms in a landscape drive stable patterns of temperature and moisture, and correlate with exposure, nutrient availability, and soil depth (Barnes et al.1982, Forman 1995). 

The basic landform unit (a.k.a. ecological land unit, land facet, land segment, elementary landform, or relief unit) is the smallest homogeneous division of the land surface at a given scale. Because each unit is characterized by attributes such as elevation, slope, aspect, exposure, moisture, and topographic position, they can be used at a proxy for topographically-based micro climates and the number and variety in an area can provide an estimate of the number of microclimates available to species. 

To map landforms and quantify microclimates, we developed a GIS model that divides and classifies a continuous terrain surface into one of 16 landforms. Our methods are based on those of Fels and Matson (1997), and are described in detail elsewhere (Anderson 1999, Anderson et al. 2012). 

We used a 10-m digital elevation model as our terrain input. This DEM was created for Hawaii by mosaicking source 1/3 arc second DEM geoTIFFs downloaded from USGS National Map Downloader
https://viewer.nationalmap.gov/basic/#productGroupSearch3/19/2020. The mosaic result was projected to our Hawaii projection NAD83 projection and resampled to 10m using bilinear resampling. As a final step before landform generation, we ran a standard 3x3 cell low-pass filter on the 10m which reduces the significance of anomalous cells and we filled sinks for the further terrain and hydrological modeling. 
We derived estimates of slope, aspect, land position, and a moisture index for each 10m cell in the study area using this statewide 10m DEM. For slope, aspect, and land position, we defined thresholds that allowed us to partition values into different major landform zones that corresponded with recognizable distinctions of landforms in the field. The primary divisions in the model were based on relative land position and slope. Some slope classes were then further divided by aspect. Sideslopes and flats were further divided by the moisture index.
Each of these DEM derivatives are described below:

• Topographic Position Index: The Topographic Position Index (TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell. For example, if the model cell was, on average, higher than the surrounding cells, then it was considered closer to the hill top (a more positive position value), and conversely, if the model cell was, on average, lower than the surrounding cells, then it was considered closer to the slope bottom (a more negative position value). In Hawaii, as in our other Lower 48 and Alaska landform work, we evaluated the TPI elevation differences between any cell and the surrounding cells within a search radius of 300m. The final index value was multiplied by 10,000 and intergerized before creating the following classes high to low topographic land position four classes: -976<=, -975 -15 , -14 975, >=976 
• Slope: Degree of slope was calculated as the difference in elevation between two neighboring cells, expressed in degrees. The continuous slope was classified into the following groups. Thresholds were consistent with others used in the Lower 48 and Alaska except for the highest two classes which were adjusted to better represent cliff and steep slope patterning in Hawaii with our 10m DEM: 0-2 2-6, 6-25, 25-40, >40
• Aspect: Aspect was calculated using the GIS Aspect tool which fits a plane to the z-values of a 3 x 3 cell neighborhood around a center cell. The direction the plane faces is the aspect for the center cell. The continuous aspect was divided into general north and south facing aspects as follows: 90-270 = 1, 0 90= 2 & 270-360 = 2
• Moisture index: We calculated a moisture index following a topographic wetness index formula, also known as the compound topographic index (CTI). This wetness index is a steady state wetness index commonly used to quantify topographic control on hydrologic processes. It uses upstream flow accumulation and slope in the formulaMoisture index = ln [(flow accumulation + 1) / (slope + 1)]. The resultant index was then smoothed using a 3 cell radius circular focal mean. 
We then thresholded the moisture index to map areas in landscape where we would expect significantly higher soil moisture and overland upslope flow to accumulate based on the topography. We sampled the wetness index underneath known wetland vegetation and bog communities from the CAH landcover to determine the mean, standard deviation, and range of wetness index values under these known wetlands. We then selected areas > 0.5 SD above the mean wetness index (>1818) under these known wetlands to represent a likely more wet pluvial accumulation zone.Sideslope and other lower slope and landposition landforms coincident with these areas of high moisture index, were reclassified into “pluvial depression landforms”. Additionally, ephemeral and intermittent classed stream and river areas from the NHD high resolution dataset were added to this category of pluvial accumulation moister areas. These types of streams and rivers often fell on the high wetness index pixels and in some cases the stream flowlines helped connect the high moisture index identified groups of cells. These types of stream channels are also expected to be accumulation areas where water would accumulate and flow and create moist environments at certain times of year when there is adequate precipitation. 


Wetlands:
Current wetlands were then added as an additional “wetflat’ landform type. These areas were mapped using current wetland vegetation or native bog communities (value 40 and 41) from the Carbon Assessment of Hawaii Land Cover Map (CAH_LandCover) (Jacobi et al. 2017). Although the moist pluvial accumulation areas based on the moisture index provided information on where higher water accumulation was likely, we felt the actual wetland vegetation areas from the most recent landcover provided additional validation to denote particularly extremely wet areas where unique wetlands were validated. These areas might also include in some cases groundwater fed wetlands in areas that would be wet not due to overland flow based on topography (as our moisture index would pick up) but due to underground groundwater processes. 

Water: Rivers/streams, and Lakes/ponds
Lastly, the natural extent of freshwater streams, rivers, and lakes were incorporated into the landform dataset as a single “freshwater” class. Sources for water included 
1. Water Class from the Carbon Assessment of Hawaii Land Cover Map (CAH_LandCover): Jacobi, J.D., Price, J.P., Fortini, L.B., Gon III, S.M., and Berkowitz, Paul, 2017, Carbon Assessment of Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/F7DB80B9.
This water represented natural waterbodies and had already had non-natural small reservoirs and farm storage tanks and ponds removed.
2. National Hydrography Dataset High Resolution Flowlines: https://www.usgs.gov/national-hydrography/nhdplus-high-resolution
Perennial stream and river centerlines were extracted using a query to select "Feature_Ty" = 'STREAM/RIVER' AND "Description" = 'Hydrographic Category|perennial'
3. National Hydrography Dataset High Resolution Area: https://www.usgs.gov/national-hydrography/nhdplus-high-resolution
Perennial stream an river wide area polygons were extracted using a query on FTYPES as follows: Stream/River 460, Canal/Ditch 336, Area of Complex Channels 537, Rapids 431, and Inundation Area 403


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Layers: Description:
Landforms are natural features of the earth’s surface created by topography - collectively the set of landforms comprises a region’s terrain. A single landform can be described as a combination of topographic position, aspect, slope, and moisture (e.g., moist north-facing toeslope). The distribution of landforms in a landscape drive stable patterns of temperature and moisture, and correlate with exposure, nutrient availability, and soil depth (Barnes et al.1982, Forman 1995). 

The basic landform unit (a.k.a. ecological land unit, land facet, land segment, elementary landform, or relief unit) is the smallest homogeneous division of the land surface at a given scale. Because each unit is characterized by attributes such as elevation, slope, aspect, exposure, moisture, and topographic position, they can be used at a proxy for topographically-based micro climates and the number and variety in an area can provide an estimate of the number of microclimates available to species. 

To map landforms and quantify microclimates, we developed a GIS model that divides and classifies a continuous terrain surface into one of 16 landforms. Our methods are based on those of Fels and Matson (1997), and are described in detail elsewhere (Anderson 1999, Anderson et al. 2012). 

We used a 10-m digital elevation model as our terrain input. This DEM was created for Hawaii by mosaicking source 1/3 arc second DEM geoTIFFs downloaded from USGS National Map Downloader
https://viewer.nationalmap.gov/basic/#productGroupSearch3/19/2020. The mosaic result was projected to our Hawaii projection NAD83 projection and resampled to 10m using bilinear resampling. As a final step before landform generation, we ran a standard 3x3 cell low-pass filter on the 10m which reduces the significance of anomalous cells and we filled sinks for the further terrain and hydrological modeling. 
We derived estimates of slope, aspect, land position, and a moisture index for each 10m cell in the study area using this statewide 10m DEM. For slope, aspect, and land position, we defined thresholds that allowed us to partition values into different major landform zones that corresponded with recognizable distinctions of landforms in the field. The primary divisions in the model were based on relative land position and slope. Some slope classes were then further divided by aspect. Sideslopes and flats were further divided by the moisture index.
Each of these DEM derivatives are described below:

• Topographic Position Index: The Topographic Position Index (TPI) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell. For example, if the model cell was, on average, higher than the surrounding cells, then it was considered closer to the hill top (a more positive position value), and conversely, if the model cell was, on average, lower than the surrounding cells, then it was considered closer to the slope bottom (a more negative position value). In Hawaii, as in our other Lower 48 and Alaska landform work, we evaluated the TPI elevation differences between any cell and the surrounding cells within a search radius of 300m. The final index value was multiplied by 10,000 and intergerized before creating the following classes high to low topographic land position four classes: -976<=, -975 -15 , -14 975, >=976 
• Slope: Degree of slope was calculated as the difference in elevation between two neighboring cells, expressed in degrees. The continuous slope was classified into the following groups. Thresholds were consistent with others used in the Lower 48 and Alaska except for the highest two classes which were adjusted to better represent cliff and steep slope patterning in Hawaii with our 10m DEM: 0-2 2-6, 6-25, 25-40, >40
• Aspect: Aspect was calculated using the GIS Aspect tool which fits a plane to the z-values of a 3 x 3 cell neighborhood around a center cell. The direction the plane faces is the aspect for the center cell. The continuous aspect was divided into general north and south facing aspects as follows: 90-270 = 1, 0 90= 2 & 270-360 = 2
• Moisture index: We calculated a moisture index following a topographic wetness index formula, also known as the compound topographic index (CTI). This wetness index is a steady state wetness index commonly used to quantify topographic control on hydrologic processes. It uses upstream flow accumulation and slope in the formulaMoisture index = ln [(flow accumulation + 1) / (slope + 1)]. The resultant index was then smoothed using a 3 cell radius circular focal mean. 
We then thresholded the moisture index to map areas in landscape where we would expect significantly higher soil moisture and overland upslope flow to accumulate based on the topography. We sampled the wetness index underneath known wetland vegetation and bog communities from the CAH landcover to determine the mean, standard deviation, and range of wetness index values under these known wetlands. We then selected areas > 0.5 SD above the mean wetness index (>1818) under these known wetlands to represent a likely more wet pluvial accumulation zone.Sideslope and other lower slope and landposition landforms coincident with these areas of high moisture index, were reclassified into “pluvial depression landforms”. Additionally, ephemeral and intermittent classed stream and river areas from the NHD high resolution dataset were added to this category of pluvial accumulation moister areas. These types of streams and rivers often fell on the high wetness index pixels and in some cases the stream flowlines helped connect the high moisture index identified groups of cells. These types of stream channels are also expected to be accumulation areas where water would accumulate and flow and create moist environments at certain times of year when there is adequate precipitation. 


Wetlands:
Current wetlands were then added as an additional “wetflat’ landform type. These areas were mapped using current wetland vegetation or native bog communities (value 40 and 41) from the Carbon Assessment of Hawaii Land Cover Map (CAH_LandCover) (Jacobi et al. 2017). Although the moist pluvial accumulation areas based on the moisture index provided information on where higher water accumulation was likely, we felt the actual wetland vegetation areas from the most recent landcover provided additional validation to denote particularly extremely wet areas where unique wetlands were validated. These areas might also include in some cases groundwater fed wetlands in areas that would be wet not due to overland flow based on topography (as our moisture index would pick up) but due to underground groundwater processes. 

Water: Rivers/streams, and Lakes/ponds
Lastly, the natural extent of freshwater streams, rivers, and lakes were incorporated into the landform dataset as a single “freshwater” class. Sources for water included 
1. Water Class from the Carbon Assessment of Hawaii Land Cover Map (CAH_LandCover): Jacobi, J.D., Price, J.P., Fortini, L.B., Gon III, S.M., and Berkowitz, Paul, 2017, Carbon Assessment of Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/F7DB80B9.
This water represented natural waterbodies and had already had non-natural small reservoirs and farm storage tanks and ponds removed.
2. National Hydrography Dataset High Resolution Flowlines: https://www.usgs.gov/national-hydrography/nhdplus-high-resolution
Perennial stream and river centerlines were extracted using a query to select "Feature_Ty" = 'STREAM/RIVER' AND "Description" = 'Hydrographic Category|perennial'
3. National Hydrography Dataset High Resolution Area: https://www.usgs.gov/national-hydrography/nhdplus-high-resolution
Perennial stream an river wide area polygons were extracted using a query on FTYPES as follows: Stream/River 460, Canal/Ditch 336, Area of Complex Channels 537, Rapids 431, and Inundation Area 403


Copyright Text: Center for Resilient Conservation Science, The Nature Conservancy.

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