Do airborne laser scanning biomass prediction models benefit from Landsat time series, hyperspectral data or forest classification in tropical mosaic landscapes?

Airborne laser scanning (ALS) is considered as the most accurate remote sensing data for the predictive modelling of AGB. However, tropical landscapes experiencing land use changes are typically heterogeneous mosaics of various land cover types with high tree species richness and trees outside forests, making them challenging environments even for ALS. Therefore, combining ALS data with other remote sensing data, or stratification by land cover type could be particularly beneficial in terms of modelling accuracy in such landscapes. Our objective was to test if spectral-temporal metrics from the Landsat time series (LTS), simultaneously acquired hyperspectral (HS) data, or stratification to the forest and non-forest classes improves accuracy of the AGB modelling across an Afromontane landscape in Kenya. The combination of ALS and HS data improved the cross-validated RMSE from 51.5 Mg ha−1 (42.7%) to 47.7 Mg ha−1 (39.5%) in comparison to the use of ALS data only. Furthermore, the combination of ALS data with LTS and HS data improved accuracies of the models for the forest and non-forest classes, and the overall best results were achieved when using ALS and HS data with stratification (RMSE 40.0 Mg ha−1, 33.1%). We conclude that ALS data alone provides robust models for AGB mapping across tropical mosaic landscapes, even without stratification. However, ALS and HS data together, and additional forest classification for stratification, can improve modelling accuracy considerably in similar, tree species rich areas.Peer reviewe

Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory

Remote sensing (RS) has enhanced forest inventory with model-based inference, that is, a family of statistical procedures rigorously estimates the parameter of a variable of interest (VOI) for a spatial population, e.g., the mean or total of forest carbon for a study area. Upscaling in earth observation, alias to this estimation, aggregates VOI from a finer spatial resolution to a coarser one with reduced uncertainty, serving decision making for natural resource management at larger scales. However, conventional model-based estimation (CMB) confronts a major challenge: it only supports RS wall-to-wall data, meaning that remotely sensed data must be available in panorama and non-wall-to-wall but quality data such as lidar or even cloud-masked satellite imagery are not supported due to incomplete coverage, impeding precise upscaling with cutting-edge instruments or for large scale applications. Consequently, this study aims to develop and demonstrate the use and usefulness of RS nonwall-to-wall data for upscaling with Hierarchical model-based estimation (HMB) which incorporates a two-stage model for bridging RS non- and wall-to-wall data; and for optimizing cost-efficiency, to evaluate the effects of non-wall-to-wall sample size on upscaling precision. Three main conclusions are relevant: (1) the HMB is a variant of the CMB estimator through trading in the uncertainty of the second-stage model to enable estimation using RS non-wall-to-wall data; (2) a quality first-stage model is key to exerting the advantage of HMB relative to the CMB estimator; (3) the variance of the HMB estimator is dominated by the first-stage model variance component, indicating that increasing the sample size in the first-stage is effective for increasing the overall precision. Overall, the HMB estimator balances tradeoffs between cost, efficiency and flexibility when devising a model-based upscaling in earth observation

Airborne and spaceborne remote sensing for assessment of forest structural attributes across tropical mosaic landscapes

High-resolution, accurate, and updated forest structure maps are urgently required for the implementation of REDD+, payment of ecosystem services, and other climate change mitigation strategies in tropical countries. The collection of forest inventory data is usually labor intensive and costly, and remote sites can be difficult to access. Remote sensing data, for example airborne laser scanning (ALS), hyperspectral imagery, and Landsat data, complement field-based forest inventories and provide high-resolution, accurate, and spatially explicit data for mapping forest structural attributes. However, issues such as the effect of topography, pulse density, and the single and combined use of various remote sensing data on forest structural attributes prediction warrant further research. The main objective of this thesis was to assess airborne and spaceborne remote sensing techniques for modeling forest structural attributes across a montane forest landscape in the Taita Hills, Kenya. The sub-objectives focused on a) the effect of the topographic normalization of Landsat images on fractional cover (Fcover) prediction, aboveground biomass (AGB), and forest structural heterogeneity modeling using ALS and other remote sensing data and b) the analysis of the maps of forest structural attributes. In Study I, the effect of topographic normalization on ALS-based Fcover modeling was evaluated using common vegetation indices and spectral-temporal metrics based on a Landsat time series (LTS). The results demonstrate that the fit of the Fcover models did not improve after topographic normalization in the case of ratio-based vegetation indices (Normalized Difference Vegetation Index, NDVI; reduced simple ratio, RSR) or tasseled cap (TC) greenness; however, the fit improved in the case of brightness and wetness, particularly in the period of the lowest sun elevation. However, if TC indices are preferred, then topographic normalization using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is recommended. In Study II, field-based AGB estimates are modeled by ALS data and a multiple linear regression. The plot-level AGB was modeled with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 52.9 Mg ha-1. Furthermore, the determinants for AGB spatial distribution are examined using geospatial data and statistical modeling. The AGB patterns are controlled mainly by mean annual precipitation (MAP), the distribution of croplands, and slope, which collectively explained 69.8% of the AGB variation. Study III investigated whether the fusion of ALS with LTS and hyperspectral data, or stratification of the plots to the forest and non-forest classes, improves AGB modeling. According to the results, the prediction model based on ALS data only provides accurate models even without stratification. However, using ALS and HS data together, and employing an additional forest classification for stratification, improves the model accuracy considerably in the studied landscape. Finally, in Study IV, the potential of single and combined ALS and LTS data in modeling forest structural heterogeneity (the Gini coefficient of tree size) was assessed, and the difference between three forest remnants and forest types is evaluated based on predicted maps. If the LTS metrics were included in the models, then ALS data with lower pulse density yield similar accuracy to more expensive, high pulse-density data. Furthermore, the GC map presents forest structural heterogeneity patterns at the landscape scale a

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.Peer reviewe