High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

Abstract

High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence.We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81-98% true positive rate and 90-98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0-70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California. history in California, beginning with the founding of the first state park in 1901 following intensive and unregulated logging of old-growth redwoods during the second half of the eighteenth century. While some old-growth forests were protected, logging in many redwood forest regions in California continued until the 1970s, when the passing of the Endangered Species Act increased the cost of obtaining a permit to harvest redwoods [4]. Currently, approximately 90% of extant redwood forest is second-growth forest that has been logged at some point during the last two centuries. Redwood forests are fragmented by past harvest history and current land use pressures, and accordingly, there is a need for local-scale, high resolution management of redwood forest land. Maps of redwood distributions are currently available at 1-km spatial resolution, but due to the complex topography, geology, and climate of coastal California, the abundance and properties of redwoods can vary enormously within a 1 km 2 area. Higher-resolution maps have not yet been produced due to the challenge of conducting field work to identify trees over large areas in topographically complex regions.Remote sensing image analysis provides an array of potential tools for mapping vegetation properties [5]. Variation in sensor type, the spectral resolution of the sensor and the spatial resolution of the data have different applications. Passive sensors such as the Landsat Multispectral Scanner (MSS), have been used for mapping vegetation types at the community level and regional scales [6]. Active remote sensing has also been applied f...

Landscape scale variation in the hydrologic niche of California coast redwood

“…Field training data were collected from 362 redwood crowns and 268 nonredwood crowns, and were used to train a model to classify pixels as 'redwoods' or 'other' across a hyperspectral image. The classification model, when tested on a dataset that was not included in model development, mapped redwood pixels with a 98% true positive rate (TPR) and 2% false positive rate (FPR) at Big Basin, 90% TPR and 1% FPR at Muir Woods, and 81% TPR and 3% FPR at Jackson Forest (Francis and Asner 2019). The model was then applied to the hyperspectral image for each site.…”

Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

“…In recent years, the use of hyperspectral remote sensing (HRS) data has become common for identifying the composition and physiognomy of forests across large areas [4][5][6][7]. The high spectral resolution of HRS (50-100 nm band width) allows identification of each classified land-cover cluster according to its known spectral signature, thereby enabling detailed analyses of land covers such as mineral composition [8], soil type [9], and vegetation structure and composition [8][9][10][11][12][13][14][15][16][17][18][19].…”