Using landsat spectral indices in time-series to assess wildfire disturbance and recovery

Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass's delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy 'greenness', are not as reliable, with values returning to pre-fire levels in 3-5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8-10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and lim

Characterizing forest biomass and the impacts of bark beetles and forest management in the southern Rocky Mountains, USA

Includes bibliographical references.2020 Summer.To view the abstract, please see the full text of the document

Using earth observation satellites to explore forest dynamics across large areas

A third of the land on earth is covered by forests. Forests provide valuable resources and essential ecosystem services, including filtering air and water, harbouring biodiversity and managing the carbon cycle. Regular monitoring and reporting across various indicators is necessary to manage forests sustainably. Due to the vastness of forests, satellite Earth observation is one of the most practical and cost-effective ways to monitor forests. The regular and consistent measurements provided from space enable time series analysis, which can reveal trends over time. The temporal, spatial and radiometric depth of the Landsat archive, which extends back to 1972 in some cases, is one of the most useful resources for monitoring forest dynamics across large areas. Analysing forest disturbance and recovery trends using Landsat has recently become widespread, particularly since the opening of the image archive in 2008. However, deriving useful information from the data is challenging on many fronts, including overcoming cloud-cover, differentiating true changes from noise and relating spectral measurements to meaningful outputs. In addition, large data volumes create hurdles for processing and storage. This study presents new techniques for exploiting the Landsat archive in relation to monitoring and measuring forest disturbance and recovery across large areas. Landsat data were processed through a series of steps, analysed in time series, and combined with other data sources to produce mapped outputs and statistical summaries, which can be interpreted by non-experts. The spatial extent of the analysis expands across multiple scales - from local and regional to global (temperate and boreal forests). Firstly, eight Landsat spectral indices were assessed to determine their sensitivity to forest disturbance (caused by wildfire) and recovery in southeast Australian forests. Results indicated that indices making use of the shortwave infrared wavelengths were more reliable indicators of forest disturbance and recovery than indices using only the red and near-infrared wavelengths. Following this exploratory analysis, three indices and two change detection algorithms were evaluated in terms of their ability to detect forest disturbance. Results showed that the LandTrendr algorithm with the Normalised Burn Ratio (NBR) was the most accurate single algorithm/index combination (overall error 21%). However, results were greatly improved by using an ensemble approach. A Random Forests model combining several Landsat-derived metrics with multiple indices, trained with human interpreted reference data, had an overall error of 7%. A notable finding was that priming the training data with confusing cases (commission errors from the change detection algorithms) led to increased accuracy. One Random Forests model was used to create annual forest disturbance maps (1989-2017) across the state of Victoria, Australia. These maps, in conjunction with each pixel's temporal trajectory, were used to extract metrics for spectral disturbance magnitude and recovery length across 2 million ha of burned forest in southeast Australia. The association between disturbance magnitude and forest recovery length, as measured spectrally, was then explored. A novel patch-based technique was used to isolate the disturbance-recovery relationship from confounding factors such as climate, elevation and soil type. The results showed statistically significant differences across bioregions and forest types. The patch-based method demonstrated how Landsat time series can be harnessed to explore ecological changes. The methods developed above were then employed over a much larger area, to investigate trends in fire disturbance and forest recovery in temperate and boreal forests worldwide. This work used both MODIS and Landsat data, through the Google Earth Engine platform, to look at trends in burned area, fire severity and forest recovery across almost 2 billion ha of forests, over the last 18 years. Burned area results showed significant increasing trends in two cases: coniferous forests in Canada and Mediterranean forests in Chile. A significant decreasing trend was found in temperate mixed forests in China. An assessment of fire severity, as measured by Landsat spectral change, highlighted possible trends in a few cases; most notably, the Russian taiga, where increasing severity was observed. An analysis of forest recovery, based on Landsat time series, indicated recovery times were accelerating in many regions. However, given the relatively short time-period analysed, these results should be interpreted with caution. The results presented in this thesis demonstrate the power of Earth observation satellites in monitoring forests at the landscape scale. Although forests are complex systems that are influenced by a myriad of factors, the regular and consistent measurements provided by satellites can be analysed in time series to provide inter-comparable results across large areas. This can broaden our understanding of the dynamic nature of forests, and in doing so, help progress towards their sustainable management

The use of remote sensing for characterizing forests in wildlife habitat modeling

Spatially explicit maps of habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Although the use of remote sensing in wildlife studies has increased in recent years, the use of these datasets is still limited and some data sources and methods are yet to be explored. The overall goal of this dissertation was to look at the state of the wildlife ecology discipline in the use of geospatial data for habitat mapping, and to advance this area through the fusion of remote sensing tools for the mapping of previously difficult to characterize forest metrics for inclusion in avian cavity-nester habitat models. Chapter 2 reviewed over 60 years of selected wildlife literature to examine the wildlife ecology disciple through historic trends and recent advances in the use of remote sensing for habitat characterization focusing on aspects of scale and the use of available technology. We discuss commonly used remote sensing data sources, point out recent advances in the use of geospatial data for characterizing forest wildlife habitat (the use of lidar data and the creation of spatially explicit habitat prediction maps), and provide future suggestions for increased utilization of available datasets (secondary lidar metrics and time series Landsat data). In chapters 3 and 4 we explored the use of remote sensing for characterizing forest components previously difficult to map across landscapes at scales relevant to local wildlife habitat selection. Chapter 3 found promise in the fusion of lidar structure and Landsat time series disturbance products in the modeling and mapping of post-fire snag and shrub distributions at fine scales and at size/cover thresholds relevant for habitat mapping applications for many wildlife species. The study was conducted within the 2003 B&B Fire Complex in central Oregon. Using 164 field calibration plots and remote sensing predictors, we modeled the presence/absence of snag classes (dbh ≥40cm, ≥50cm, and ≥75cm) and woody shrub cover resulting in 10m output predictive grid maps. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest conditions. We were able to model and map all habitat metrics with acceptable predictive performance and low-moderate errors. The utility of these snag and shrub metrics for representing important nesting habitat features for a cavity-nesting species of conservation concern, the Lewis's Woodpecker (Melanerpes lewis), was demonstrated in Chapter 4. We were able to model nesting habitat with good accuracies according to multiple performance measures and then map realized relationships for this species of conservation concern in an identified source habitat type, providing a potential resource for local scale conservation and management efforts and adding to the regional knowledge of habitat selection for the Lewis's Woodpecker. To our knowledge, these chapters represent first attempts to fuse lidar and time series Landsat disturbance metrics in a post-fire landscape and for the mapping of snag and shrub distributions at scales relevant to avian cavity nesting habitat