Search

Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern forest management methods threaten these ancient monuments and cultural remains. In northern Europe, older forests often contain very old traces, such as millennia-old hunting pits and indigenous Sami hearths. Investigations have repeatedly found that forest owners often fail to protect these cultural remains and that many are damaged by forestry operations. Current maps of hunting pits are incomplete, and the locations of known pits have poor spatial accuracy. This study investigated whether hunting pits can be automatically mapped using national airborne laser data and deep learning. The best model correctly mapped 70% of all the hunting pits in the test data with an F1 score of 0.76. This model can be implemented across northern Scandinavia and could have an immediate effect on the protection of cultural remains. [ABSTRACT FROM AUTHOR]

Comparisons between field data and available maps show that 64% of wet areas in the boreal landscape are missing on current maps. Primarily forested wetlands and wet soils near streams and lakes are missing, making them difficult to manage. One solution is to model missing wet areas from high-resolution digital elevation models, using indices such as topographical wetness index and depth to water. However, when working across large areas with gradients in topography, soils and climate, it is not possible to find one method or one threshold that works everywhere. By using soil moisture data from the National Forest Inventory of Sweden as a training dataset, we show that it is possible to combine information from several indices and thresholds, using machine learners, thereby improving the mapping of wet soils (kappa = 0.65). The new maps can be used to better plan roads and generate riparian buffer zones near surface waters. [ABSTRACT FROM AUTHOR]

This study focuses on avoiding negative effects on surface waters using new techniques for identifying wet areas near surface waters. This would aid planning and designing of forest buffer zones and off-road forestry traffic. The temporal variability in the geographical distribution of the stream network renders this type of planning difficult. A field study was performed in the 68 km2 Krycklan Catchment to illustrate the variability of a boreal stream network. The perennial stream length was 140 km while the stream length during high-flow conditions was 630 km. Comparing the field-measured stream network to the network presented on current maps showed that 58% of the perennial and 76% of the fully expanded network was missing on current maps. Similarly, cartographic depth-to-water maps showed that associated wet soils constituted 5% of the productive forest land during baseflow and 25% during high flow. Using a new technique, maps can be generated that indicate full stream networks, as well as seasonally active streams and associated wet soils, thus, forestry planning can be performed more efficiently and impacts on surface waters can be reduced. [ABSTRACT FROM AUTHOR]

• A new method for accurate high-resolution soil moisture mapping in forests. • LIDAR-derived terrain indices proved effective for mapping of forest soil moisture. • Extreme Gradient Boosting was the best algorithm for mapping soil moisture. • The continuous soil moisture map would significantly contribute to practical forestry. • Maps were relatively less accurate near roads, on coarse sediments, and in steep terrains. Spatially extensive high-resolution soil moisture mapping is valuable in practical forestry and land management, but challenging. Here we present a novel technique involving use of LIDAR-derived terrain indices and machine learning (ML) algorithms capable of accurately modeling soil moisture at 2 m spatial resolution across the entire Swedish forest landscape. We used field data from about 20,000 sites across Sweden to train and evaluate multiple ML models. The predictor features (variables) included a suite of terrain indices generated from a national LIDAR digital elevation model and ancillary environmental features, including surficial geology, climate and land use, enabling adjustment of soil moisture class maps to regional or local conditions. Extreme gradient boosting (XGBoost) provided better performance for a 2-class model, manifested by Cohen's Kappa and Matthews Correlation Coefficient (MCC) values of 0.69 and 0.68, respectively, than the other tested ML methods: Artificial Neural Network, Random Forest, Support Vector Machine, and Naïve Bayes classification. The depth to water index, topographic wetness index, and 'wetland' categorization derived from Swedish property maps were the most important predictors for all models. The presented technique enabled generation of a 3-class model with Cohen's Kappa and MCC values of 0.58. In addition to the classified moisture maps, we investigated the technique's potential for producing continuous soil moisture maps. We argue that the probability of a pixel being classified as wet from a 2-class model can be used as a 0–100% index (dry to wet) of soil moisture, and the resulting maps could provide more valuable information for practical forest management than classified maps. [ABSTRACT FROM AUTHOR]