Linking Optical SPOT and Unmanned Aerial Vehicle data for a rapid biomass estimation in a Forest-savanna Transitional Zone of Ghana

The direct estimation of biomass using remote sensing technologies, such as LiDAR, RaDAR and Stereo Data is limited in utility, since it does not allow for historical analysis of biomass dynamics far back in time due to their recency in development. This study links Unmanned Aerial Vehicle (UAV)-measured tree height and optical SPOT image reflectance in a mathematical model for a quick and less expensive indirect biomass estimation, and the possibility of historical analysis using the earliest captured optical data. SPOT 6/7 images were used to map land-use/cover patterns. A Phantom 4 drone images were used for height and crown width estimation. A stepwise regression analysis was conducted to establish a relationship between SPOT 6/7 channels and the UAV-generated tree heights. The linear model was used to convert the reflectance values of SPOT images into tree heights, and in turn used for crown width estimation. The estimated tree height and crown width images were used to estimate biomass using an allometric equation. There was no statistically significant difference between UAV and manual tree height measurements. UAV-estimated tree height predicted 88.0% of crown width. Regressing the tree height on the SPOT bands yielded an R2 of 66.0%. It is recommended that further studies be conducted to improve on the accuracy of estimation. It is hoped this would facilitate a quick biomass estimation and long term historical dynamics

Application of satellite image time series and texture information in land cover characterization and burned area detection

Land cover is critical information to various land management and scientific applications, including biogeochemical and climate modeling. In addition, fire is an essential factor in shaping of vegetation structures, as well as for the functioning of savanna ecosystems. Remote sensing has long been an important and effective means of mapping and monitoring land cover and burned area over large areas in a consistent and robust way. Owing to the free and open Landsat archive and the increasing availability of high spatial resolution imagery, seasonal features from the temporal domain and the use of texture features from the spatial domain create new opportunities for land cover characterization and burned area detection. This thesis examined the application of satellite image time series and texture information in land cover characterization and burned area detection. First, the utility of seasonal features derived from Landsat time series (LTS) in improving accuracies of land cover classification and attribute prediction in a savanna area in southern Burkina Faso was studied. Then, the temporal profiles from LTS were explored for mapping burned areas over a 16 year period, and MODIS burned area product was used for comparison. Finally, the application of texture features derived from high spatial resolution data in land cover classification and attribute predictions was investigated in a savanna area of Burkina Faso and an urban fringe area in Beijing. According to the results, firstly, seasonal features from LTS based on all available imagery during one year as input led to a significant increase in land cover classification accuracy in comparison to the dry and wet season single date imagery. The harmonic model used for time series modeling provided a robust method for extracting seasonal features, and the influence of burned pixels on seasonal features could be considered simultaneously. Secondly, the annual burned area mapping based on a harmonic model and breakpoint identification with LTS was capable of detecting small and patchy burn scars with higher accuracy than MODIS burned area product. The approach demonstrated the potential of LTS for improving burned area detection in savannas, and was robust against data gaps caused by clouds and Landsat 7 missing lines. Thirdly, predictive models of tree crown cover (CC) using RapidEye and LTS imagery achieved similar accuracy, indicating the importance of texture and seasonal features from RapidEye and LTS imagery, respectively. Predictions of aboveground carbon and tree species richness, which were strongly correlated with CC, were promising using RapidEye and LTS imagery. Finally, the optimized window size texture classification improved classification accuracy in comparison to the classifications with single window size texture features and multiple window size texture features in an urban fringe area in Beijing, indicating the importance of multiscale texture information. Keywords: Landsat time series, texture, land cover classification, burned area, savanna, tree crown cove

Inverting Aboveground Biomass?Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

International audienceLarge scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha−1. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha−1. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture–AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture–AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error &lt;20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation