Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery

The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740–950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS–AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.