Mapping of wind-thrown forests using satellite SAR images

The study focuses on investigation and evaluation of wind- thrown forest mapping using satellite remotely sensed data from three synthetic aperture radar (SAR) sensors. The study is carried out at Remningstorp, a test site in the south of Sweden dominated by coniferous forest, where trees were manual felled to simulate wind-thrown forest. The satellite data consisted of time series of HH polarized SAR images acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Radarsat-2 (C-band) and TerraSAR-X (X- band). The results from visual interpretation of SAR images acquired before and after the simulated wind-throw together with corresponding ratio images show that ALOS PALSAR HH polarized intensity images are not able to detect wind- thrown forest, probably due to too coarse spatial resolution. In contrast, the wind-thrown forest is clearly visible in the Radarsat-2 and TerraSAR-X HH polarized images, implying that it may be possible to develop a new application using these SAR data for mapping of wind-thrown forests

Potential of mapping forest damage from remotely sensed data

Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU

Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data

Wind represents a primary source of disturbances in forests, necessitating an assessment of the resulting damage to ensure appropriate forest management. Remote sensing, encompassing both active and passive techniques, offers a valuable and efficient approach for this purpose, enabling coverage of large areas while being costeffective. Passive remote sensing data could be affected by the presence of clouds, unlike active systems such as Synthetic Aperture Radar (SAR) which are relatively less affected. Therefore, this study aims to explore the utilization of bitemporal SAR data for windthrow detection in mountainous regions. Specifically, we investigated how the detection outcomes vary based on three factors: i) the SAR wavelength (X-band or C-band), ii) the acquisition period of the pre- and post-event images (summer, autumn, or winter), and iii) the forest type (evergreen vs. deciduous). Our analysis considers two SAR satellite constellations: COSMO-SkyMed (band-X, with a pixel spacing of 2.5 m and 10 m) and Sentinel-1 (band-C, with a pixel spacing of 10 m). We focused on three study sites located in the Trentino-South Tyrol region of Italy, which experienced significant forest damage during the Vaia storm from 27th to 30th October 2018. To accomplish our objectives, we employed a detailpreserving, scale-driven approach for change detection in bitemporal SAR data. The results demonstrate that: i) the algorithm exhibits notably better performance when utilizing X-band data, achieving a highest kappa accuracy of 0.473 and a balanced accuracy of 76.1%; ii) the pixel spacing has an influence on the accuracy, with COSMO-SkyMed data achieving kappa values of 0.473 and 0.394 at pixel spacings of 2.5 m and 10 m, respectively; iii) the post-event image acquisition season significantly affects the algorithm’s performance, with summer imagery yielding superior results compared to winter imagery; and iv) the forest type (evergreen vs. deciduous) has a noticeable impact on the results, particularly when considering autumn/winter dat

Mapping and monitoring of vegetation using airborne laser scanning

In this thesis, the utility of airborne laser scanning (ALS) for monitoring vegetation of relevance for the environmental sector was investigated. The vegetation characteristics studied include measurements of biomass, biomass change and vegetation classification in the forest-tundra ecotone; afforestation of grasslands; and detection of windthrown trees. Prediction of tree biomass for mountain birch (Betula pubescens ssp. czerepanovii) using sparse (1.4 points/m²) and dense (6.1 points/m²) ALS data was compared for a site at the forest-tundra ecotone near Abisko in northern Sweden (Lat. 68° N, Long. 19° E). The predictions using the sparse ALS data provided almost as good results (RMSE 21.2%) as the results from the dense ALS data (18.7%) despite the large difference in point densities. A new algorithm was developed to compensate for uneven distribution of the laser points without decimating the data; use of this algorithm reduced the RMSE for biomass prediction from 19.9% to 18.7% for the dense ALS data. Additional information about vegetation height and density from ALS data improved a satellite data classification of alpine vegetation, in particular for the willow and mountain birch classes. Histogram matching was shown to be effective for relative calibration of metrics from two ALS acquisitions collected over the same area using different scanners and flight parameters. Thus the difference between histogram-matched ALS metrics from different data acquisitions can be used to locate areas with unusual development of the vegetation. The height of small trees (0.3–2.6 m tall) in former pasture land near the Remnings¬torp test site in southern Sweden (Lat. 58° N, Long. 13° E) could be measured with high precision (standard deviation 0.3 m) using high point density ALS data (54 points/m2). When classifying trees taller than 1 m into the two classes of changed and unchanged, the overall classification accuracy was 88%. A new method to automatically detect windthrown trees in forested areas was developed and evaluated at the Remningstorp test site. The overall detection rate was 38% on tree-level, but when aggregating to 40 m square grid cells, at least one windthrown tree was detected in 77% of the cells that according to field data contained windthrown trees. In summary, this thesis has shown the high potential for ALS to be a future tool to map and monitor vegetation for several applications of interest for the environmental sector

Constraints on tree growth, impacts of tropical cyclones and outcomes of community management in the Miombo woodlands

The Miombo woodlands of southern Africa are a globally significant store of carbon (C) and biodiversity. They also provide services for more than 150M people across several of the world’s most economically impoverished countries. The Miombo woodlands are dynamic, with extensive resource loss accompanied by areas of regrowth and increase. Disturbance processes, both from natural processes and widespread anthropogenic activities, are critical in maintaining woody biomass in these ecosystems, although intensity of disturbance varies widely. Increase in woody biomass has been observed in the Miombo, though the drivers of this trend are uncertain and the fundamental constraints on trees and woodlands not well understood. Ultimately, both losses and gains can be difficult to detect and hard to attribute to a particular cause. The aim of this thesis is to use field data and remote sensing to add to understanding of the constraints on tree populations in the Miombo and the impacts of severe environmental disturbances and management interventions on woodland structure. Tree growth is a crucial demographic rate in African woodlands and plays a key role in shaping woodland structure and C cycling. However, observations of tree growth are relatively lacking in the Miombo and the determinants of tree growth rates are poorly known. In Chapter 2 I use data collected from long-term monitoring of permanent sample plots in Mozambique and Tanzania and linear mixed modelling to estimate tree growth increments and assess the relative importance of different determinants of tree growth. The estimated growth (diameter increment) in these plots was 1.8 ± 0.17 mm/yr. Climate and edaphic factors explained little variation in tree growth. Tree-tree competition was found to be a significant constraint on growth (trees in experiencing competition levels in the top 5% of values grew 1.24 ± 0.08 mm/yr slower on average than those in the bottom 5%) as was stem wounding (wounded trees grew 0.84 ± 0.04 mm/yr slower). Root symbioses (both fungal and bacterial symbionts) which aid in the uptake of nutrients were found to have a strong positive impact on growth, particularly ectomycorrhizal associations which are common to dominant species in the Miombo. The impacts of tree-tree competition and nutrient symbioses are poorly represented in biogeochemical models in these ecosystems but this analysis suggests they are critical, whilst the subtle impacts of human interaction with trees (through wounding) are also possibly underappreciated. Tropical Cyclones can have substantial long-term impacts on woodland structure in affected areas and projections indicate that the impacts of Cyclones will increase in southeastern Africa over the coming century. There are few studies which have documented the immediate impacts or long-term responses of woodland ecosystems to this damage. In Chapter 3 I analyse data from a survey of eight permanent sample plots setup explicitly to assess the damage caused by Cyclone Idai to in woodlands in Gorongosa National Park, central Mozambique. It is found that Cyclone Idai caused damage primarily to large trees, thus whilst only 2% of trees were felled these individuals represented 8.5% of overall basal area. The implications of this damage are discussed in context of the constraints on trees in these woodlands, and whilst the damage is severe it is concluded that the outcomes are highly uncertain. Whilst damage from the cyclone is substantial, detecting change in woodland structure is challenging in these ecosystems. In Chapter 4 I explore the possibility of upscaling field observations of treefall occurrence using data from a small unmanned aerial vehicle (drone) and satellite radar. Drone survey produced comparable estimates of treefall intensity to the PSP observations (in terms of fallen number of stems, fallen basal area and carbon) and allowed survey of 155 ha, capturing widespread damage across the study area. In the study area radar backscattering intensity in C-Band radar reduced in the two years after the cyclone relative to the two years before whilst interferometric coherence increased - both in agreement with radar theory - although backscattering intensity in L-Band radar increased. Whilst significant relationships were identified between change in radar data and the intensity of damage in drone surveys, there appears to be limited ability to map variations in treefall intensity across the wider landscape using this method, or to determine areal impacts on above ground C thereafter. It is concluded that repeat analysis may yield better results however. In Tanzania, Village Land Forest Reserves (VLFRs, a form of participatory forest management) aim to promote sustainable profit from woodland resources, although the impact of VLFRs on land cover change rates is uncertain. In Chapter 5 I use satellite radar to map deforestation and a degradation across an area of southern Tanzania from 2010-2018 and statistical matching to compare rates of land cover change within a sample of VLFRs to woodlands under comparable resource pressure outside VLFRs or other protection status. It is found that VLFRs in the majority of cases were very effective in reducing deforestation (with five of seven having rates close to zero) and also reduced degradation rates (though to a lesser degree). Increasing density of woody biomass in forested areas was observed in all VLFRs, but varied widely across the sample (from 0.2 - 1.5 tC ha yr-1) and was in five of seven VLFRs below that observed in woodland areas with no protection status in this region (+0.7 tC ha yr-1). Whilst it appears that VLFR establishment achieved its intended goal of sustainable profit from woodlands resources from 2010-2018, further work is required to understand variation in outcome across the observed sample. This methodology however shows promise in continued assessment of VLFR performance for this purpose