Documenting
the cumulative effects of such local-scale management activities raises
complex challenges that new technologies can help solve. In the case of
forest ditching, the full scope of the problem has largely been hidden
because existing models fail to map landscape hydrography when natural
processes no longer have primacy over channel initiation. Nevertheless,
with recent modelling advances, small-scale waterways can now be
identified and mapped using high-resolution airborne laser scanning
(ALS), combined with tools based on artificial intelligence (AI; Sit et
al. 2020).
Emerging hydrological technology
We mapped small channels across all of Sweden with a deep neural
network. The deep learning model was based on ALS data and 1607 km of
manually digitized ditches (Ågren et al. 2021). The ALS point cloud had
a last return density of 0.5-1 points m-2 and a
surface model with 1 m resolution was created by the Swedish land use
and cadastral registration authority. A high pass median filter (HPMF)
was applied to the surface model to emphasize short-range variability in
the topography. The HPMF-algorithm was implemented in Whitebox tools
(Lindsay, 2014) and operates by subtracting the value at the grid cell
at the center of the window from the median value in the surrounding
neighbourhood with a kernel of 5 cells. Negative values indicate
depressions while positive values indicate ridges. The digitized vector
lines have no width so we utilized average ditch width from a field
inventory where 2188 ditch channels were surveyed across Sweden. The
average ditch width was 2 m with a standard deviation of 1.3 m. Instead
of flagging all pixels within ~3 m of a vector line as
ditch, we utilized the HPMF to create more natural ditch labels. Pixels
within three meters of a vector line and with a HPMF value less than
-0.075 were flagged as ditch pixels.
TensorFlow 2.6 was used to build an encoder-decoder style deep neural
network, to transform the filtered HPMF images into images highlighting
the detected ditches. On the encoding path, the network learns a series
of filters, organized in layers, which express larger and larger
neighbourhoods of pixels in fewer and fewer vectors of features. After
encoding the ALS image into a spatially more compact representation, it
was again decoded by a series of learned filters performing transposed
convolutions into the final classification map. This map contains, for
every pixel in the input image, the probability that the pixel belongs
to a ditch. In order to separate channels from local depressions, we
used a conditional random field layer which learns to penalize undue
label discontinuities. This neural network model was trained using
weighted cross-entropy loss to deal with the large class imbalance
between ditch and non-ditch pixels.he model was trained on 80% of the
dataset and evaluated on 20 % of the data that were set aside for
testing. The model correctly mapped 82 % of all ditches in the test
data with a Matthew’s correlation coefficient of 0.72. The final model
was implemented using Microsoft Azure to map ditch channels across all
of Sweden in collaboration with the Swedish Forest Agency. The deep
learning method with the trained model and a reproducible example is
available from GitHub (Lidberg et al. 2021). Finally, the national ditch
map is available as open data from the Swedish Forest Agency
(https://www.skogsstyrelsen.se).
Implications and conclusions
Our results suggest that while the best available maps for Sweden
include 0.4 million km of waterways, they only identify 22% of the
channels. Automatic detection using our deep learning methods increases
this estimated network length to approximately 1.2 million km (Fig 2),
equivalent to 28 times around the world. The average channel density is
2.5 km/km2, but can be up to 15
km/km2 in the most affected areas. Of all channels in
Sweden, 67% are human-made. The anthropogenic channel network has
affected almost the entire land area of Sweden, except for the mountain
region in the northwest. In Finland, similar drainage has, on average,
reduced water storage capacity of affected areas by over 200 mm (Menberu
et al. 2016), while also representing a significant source of sediment
and nutrients to downstream waterbodies (Finér et al. 2021).
When ditches age, some of their hydrological function decrease due to
peat decomposition, vegetation in-growth, and sediment deposition. Thus,
ditch-cleaning has been established as a standard practice, with the aim
of promoting tree regeneration following clear-cutting. Since many of
the forest areas drained a century ago now are reaching the end of their
rotation period, the decision of whether or not to clean ditches
following harvest is now a matter of debate. Should we follow the forest
industry’s recommendation to increase ditch-cleaning to maintain high
forest biomass production, restore them by blocking the artificial
channels, use alternative continuous cover forestry techniques to manage
water tables, or leave ditches to develop freely? Despite a scientific
discussion about evaluating the need for artificial drainage to
stimulate forest growth as early as the 1930’s (Malmström, 1931), the
science and policy debate about ditching did not resurface until the
last decade. Except for a few isolated studies addressing the drainage
effects on hydrology, research in Sweden has essentially been absent. In
this context, path dependency (Pierson, 2000), in terms of
institutionalized management practices to increase productivity in
forestry, appears to have been locked-in as an industry standard, and
could thus be resistant to new scientific findings for the current
forest land-use policy (Löfmarck, et al. 2017).
Whatever management actions are taken today will have century-long
implications for the hydrology of northern forests. Increasing the
resilience of water and forest resources in a future climate requires
improved knowledge and better communication about the implications of
different management options. The AI technology presented here (see
concluding section) can help us identify the exact location and
extension of ditches in the boreal forest, which is a first step towards
more science-based management decisions on this massive landscape
alteration occurring during the last century. Given the prospects of a
drier future, the plan to continue to clean ditches seems risky from a
water storage context, yet the efficacy and consequences of other
management options are also highly uncertain (Kreyling et al. 2021).
Either way, the story of historical ditching in Sweden serves as a
cautionary tale about making widespread landscape changes to address a
perceived problem without carefully considering the long-term
implications. To avoid repeating short-term fixes that may cause new
locked-in effects, it is critical to evaluate what management and policy
options exist to meet the demands for freshwater resources in a future
climate while also continuing to improve our knowledge base for policy
development.
As we enter the beginning of the UN Decade on Ecosystem Restoration,
mechanistic insights into restoration targets, land-use management
policies, and improved decision-support tools hold the key to successful
implementation. As the number of ALS datasets are increasing worldwide,
applying deep learning methods to detect both natural and anthropogenic
stream networks can become standard practice across many landscapes.
Based on this, we can then begin to understand how past land-use
contributes to hydrological alteration under contemporary and future
climatic scenarios. Using such landscape scale information will allow
improved identification of areas most vulnerable to climate change and
therefore those that most effectively should be targeted for different
ecosystem restoration actions. With this information we can then develop
functional restoration methods, which can be tested and applied to solve
the challenge of securing water resources in managed boreal forest
landscape in the decades to come.
Acknowledgements
This work was supported by the Swedish research agencies Formas
(2021-02114; 2019-00173), VR (SITES), Knut and Alice Wallenberg
foundation (2018.0259), and Wallenberg AI, Autonomous Systems and
Software Program – Humanities and Society.