Ecological Indicators (Jan 2024)
Retrieval of chlorophyll content for vegetation communities under different inundation frequencies using UAV images and field measurements
Abstract
Karst wetlands are an important type of wetland among the global karst areas that suffer from serious ecosystem degradation. Chlorophyll content, an important parameter of vegetation functional traits, can indicate the photosynthetic activity, mutation and stress level, and nutritional state of vegetation. However, quantitative estimation of canopy chlorophyll content (CCC) of calcium-loving and alkali-resistant vegetation in karst wetlands using remote sensing technology for clarifying the relationship between canopy spectral reflectance and the CCC of vegetation communities remains a challenge. In this study, we propose a retrieval approach for estimating the CCC of seven typical vegetation communities in the largest karst wetland in China, using a combination of parameter/non– parameter estimation model, and unmanned aerial vehicle (UAV) multispectral images. We further evaluated the sensitivity of various spectral indices to the CCC of the vegetation communities and explored the influence of image feature distribution on CCC estimation accuracy. We revealed the variation patterns of leaf chlorophyll content (LCC) and CCC under different inundation frequencies in the karst wetland. We confirmed that the UAV-based inversion method proposed in this study achieved the CCC estimation of seven vegetation communities (R2 = 0.57 ∼ 0.93, RMSE = 8.80 SPAD ∼ 60.68 SPAD), and obtained high estimation accuracy (R2 = 0.77, RMSE = 22.04 SPAD) of Cladium chinense Nees (CCN, endemic vegetation in karst wetland). We found that the optimal reflectance ranges for estimating the CCC of the seven vegetation communities were 714 nm ∼ 746 nm and 814 nm ∼ 866 nm, and that the ChIrededge and MSR indices negatively influenced the CCC estimation of CCN and Carex baccans Nees (CBN), respectively. We demonstrated that vegetation communities with well-defined canopy structure (Triadica sebifera (Linnaeus) Small) achieved higher estimation accuracy with moderate uncertainty (ε = 10.80 % ∼ 15.49 %) than the herbaceous vegetation communities (CCN, CBN, and Cynodon dactylon (L.) Pers.). The bias of the CCC estimation based on linear regression and partial least squares regression models in different vegetation communities ranged from 1.61 % to 49.41 %. We confirmed that the magnitude of variations in LCC and CCC for these vegetation communities presented a decreasing pattern from that in high-frequency inundation communities to that in the low-frequency ones.
