Post Wildfire Vegetation Response to the Wildland-Urban Interface: A Case Study of the Station Fire

In the past, wildfires served as a method for mother nature to promote biodiversity and to help maintain a functioning ecosystem. However, climate change alters the fire regime, significantly impacting vegetation recovery. Human disturbances and increased land use and land cover heighten vegetation disruption and abundance after a fire. Wildland-urban interface (WUI) – the region where the vegetation intermingles with the roads, houses, and human-made structures – threatens vegetation and the human population. Overall vegetation recovery after the Station Fire of 2009 spread through the San Gabriel Mountains, Los Angeles County was observed using Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Burn Ratio (nDBR) spectral indices. In addition, Light Detection and Ranging (LiDAR) images were used to measure aboveground biomass (AGB). The study analyzed vegetation biomass recovery by comparing human disturbances and the level of fire severity within the Station Fire perimeter. Low and moderate fire severity were compared in detail against WUI and non-WUI regions by quantifying the amount of biomass in the specified regions. Linear regression model results showed vegetation recovery rates were slower in WUI regions than in non-WUI regions despite having similar regeneration patterns while AGB rebound was similar across both region categories

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

The age of forest stands is critical information for many aspects of forest management and conservation but area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between 58{\deg} and 65{\deg} northern latitude in a 181,773 km2 study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root-mean-squared-errors (RMSE) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between -1 and 3 years. The models improved with increasing SI and the RMSE were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot-level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%). Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI, that could be considered for practical applications but see considerable potential for improvements, if better SI maps were available

Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

Plant ecology and biodiversity research have increasingly incorporated trait-based approaches and remote sensing. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on airborne LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the ITC- and pixel-based approaches at changing pixel size and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by airborne Pushbroom Hyperspectral Imager-3 (PHI-3) hyperspectral data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC-based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness also decreased with increasing pixel size, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, high-resolution pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage

Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia

Mapping and inventory of the distribution and composition of mangrove vegetation structures are crucial in managing mangrove ecosystems. The availability of airborne LiDAR remote sensing technology provides capability of mapping vegetation structures in three dimensions. It provides an alternative data source for mapping and inventory of the distribution of mangrove ecosystems. This study aims to test the ability of airborne LiDAR data to classify mangrove vegetation structures conducted in Ratai Bay, Pesawaran District, Lampung Province. The classification system applied integrates structure attributes of lifeforms, canopy height, and canopy cover percentage. Airborne LiDAR data are derived into canopy height models (CHM) and canopy cover percentage models, then grouped by examining statistics and the zonation distribution of mangroves in the study area. The results of this study show that airborne LiDAR data are able to map vegetation structures accurately. The canopy height model derived using a pit-free algorithm can represent the maximum tree height with an error range of 3.17 meters and 82.3-88.6% accuracy. On the other hand, the canopy cover percentage model using LiDAR Fraction Cover (LFC) tends to be overestimate, with an error range of 16.6% and an accuracy of 79.6-94.7%. Meanwhile, the classification results of vegetation structures show an overall accuracy of 77%

Spatial replication and habitat context matters for assessments of tropical biodiversity using acoustic indices

Approaches to characterise and monitor biodiversity based on the sound signals of ecosystems have become popular in landscape ecology and biodiversity conservation. However, to date, validation studies of how well acoustic indices reflect observed biodiversity patterns have often relied on low levels of either spatial or temporal replication, while focussing on habitats with similar underlying anthropological and geophysical sound characteristics. For acoustic indices to be broadly applicable to biodiversity monitoring, their capacity to measure the ecological facets of soundscapes must be robust to these potential sources of bias. Using two contrasting recording approaches, we examined the efficacy of four commonly used acoustic indices to reflect patterns of observed bird species richness across a tropical forest degradation gradient in Northeast Borneo. The gradient comprised intact and logged forests, riparian forests, remnants, and oil palm plantations, thus providing a highly variable anthrophonic and geophonic soundscape. We compared the degree to which acoustic indices derived from automated versus point count recording methods detected variation in inter-habitat species richness, as well as their capacity to capture changes in species diversity as a consequence of forest degradation quantified by high-resolution LiDAR derived forest canopy heights. We found Acoustic Diversity Index was associated with forest canopy height as measured by both automated recorders and recordings from point counts, whereas the association between canopy height and Acoustic Complexity Index was only detected using point count recordings. For both types of recordings, Acoustic Complexity Index exhibited the strongest relationship with observed bird richness in old growth and logged forest, whereas Acoustic Diversity was not linked, suggesting avian richness does not drive its association with canopy height. No acoustic indices were associated with observed bird richness in oil palm riparian areas. Our findings underscore the potential utility of soundscape approaches to characterise biodiversity patterns in degraded tropical landscapes, and may be used as a proxy for human inventories of bird communities. However, we also show that for acoustic indices to be effective on landscape-wide studies of environmental gradients, adequate spatial replication is required, and care must be taken to control for non-target elements of soundscapes in different habitats

Mapping relict arctic-alpine tundra vegetation from multitemporal LiDAR data

Práce se zabývá parametry vertikální struktury vegetace odvozenými z UAV LiDARových dat a jejich využitím k multitemporální klasifikaci vybraných druhů arkto-alpinské tundry Krkonoš. Na základě rešerše literatury se zaměřením na nízké a keřovité porosty jsou vytipovány strukturní parametry. Jejich vhodnost k rozlišení tundrovité vegetace je hodnocena algoritmem Random Forest a metodou určení důležitosti prediktorů pomocí permutace out-of bag pozorování, vynecháním prediktoru a individuální výkonností prediktoru. Následně je provedena fúze s multispektrálními daty a určen vliv LiDAR odvozených strukturních parametrů na zpřesnění výsledků klasifikace. Zkoumány jsou také strukturní parametry vegetace odvozené z digitálního modelu povrchu získaného obrazovou korelací multispektrálních dat. Pro odlišení vegetačních tříd byl jako nejvhodnější určen parametr maximální výšky, následován minimální výškou, relativním poměrem povrchu vegetace a koeficientem variace, které dosáhly celkové klasifikační přesnosti 67,3 % pro Bílou louku a 62,3 % pro Úpské rašeliniště. Fúze s multispektrálními daty vedla k zpřesnění klasifikace do 2 %. V případě struktury vegetace odvozené z digitálního modelu povrchu bylo dosaženo stejného výsledku s výjimkou vyšších porostů. LiDARová data se neukázala jako přínosná k odlišení...The thesis focuses on metrics of vertical structure of vegetation derived from UAV LiDAR data and their use for multitemporal classification of selected species of arctic-alpine tundra in the Krkonoše Mountains. The metrics are selected based on a literature search focusing on low and shrubby stands. Random Forest algorithm and permutation feature importance, drop column importance and individual predictor performance is used to determine the suitability of metrics for distinguishing tundra vegetation. Subsequently, a fusion with multispectral data is performed and influence of the LiDAR derived variables on the refinement of classification results is determined. The use of metrics derived from a digital surface model obtained by image correlation of multispectral data is also examined. Maximum height followed by minimum height, canopy relief ratio and coefficient of variation yielded the best results, they achieved an overall classification accuracy of 67.3% for Bílá louka meadow and 62.3% for Úpské rašeliniště bog. Fusion with multispectral data led to an increase in overall accuracy up to 2 %. In case of vegetation structure derived from the digital surface model, similar results were achieved apart from higher stands. LiDAR data did not prove to be beneficial in distinguishing grass communities...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices

Knowledge and possible application of urban vegetation patches as a technique to create biodiversity in landscape architecture

Biodiversity loss is a global issue that is due to land-use changes. A driver behind land use-changes is urbanization. To counter this development, cities create ways to support biodiversity through urban green spaces. Although urban spaces are fragmented, landscape architecture can assist the colonization of species. However, much of urban biodiversity supporting strategies focus on green corridors and connectivity. Even though corridors and patches both have significant positive effects on biodiversity, there is a lack of biodiversity supporting strategies for urban green/vegetation patches. This master thesis addresses the globally applicable factors that has the potential of creating biodiversity in and with urban vegetation patches. The thesis will also include a practical example of how these factors can be applicated in a design. The reason for including a practical example, is because there is lacking a conversion from theory to practise in this field. There can be many interpretations of how to create biodiversity in urban vegetation patches, and practical examples can improve the theoretical information by making it applicable for reality

Wildfire Exposure To Critical Habitat Of Endangered And Threatened Species In California

Researchers, fire ecologists and wildlife managers are concerned about impact to endangered and threatened species and their critical habitat due to the projected increase in future wildfires. Wildfires have been studied in California for the last six decades and have been increasing at an alarming rate since the 1980’s. In this study, I use the 2018 spatial dataset for critical habitat of federally endangered and threatened species located in the state boundaries of California and compare it to a spatial dataset for wildfires that have occurred over the span of 32 years (1984 to 2016). Trends are derived from spatial data by using ArcGIS and Tableau software. A macroscale analysis was conducted to determine what species types are most sensitive to wildfire encroachment. Then I conducted a microscale to determine which specific endangered and threatened species are threatened by wildfire to determine which recovery plans should be reviewed. Analyses indicted that critical habitat for amphibian, bird and insect endangered or threatened species types are most sensitive to wildfire encroachment. Five specific species that are more impacted than others: Arroyo southwestern toad, California red-legged frog, California condor, coastal California gnatcatcher, and Quino checkerspot butterfly. Life traits were researched for these species and recovery plans were examined for wildfire mitigation strategies. Modifications were made based on these considerations and life traits. Results and future recommendations include specific recovery plan updates that should include wildfire mitigation strategies, research on wildfire impacts on these species, and ideas with regards to how this data can be used in the best interest of the species, ecosystem, and wildlife managers

Full Issue, Volume 2

Full issue of volume 2