Land Cover Database Research Articles

An Updated Framework for Modeling White-Tailed Deer (Odocoileus virginianus) Habitat Quality in Illinois, USA.

White-tailed deer (Odocoileus virginianus) are a cervid species found mostly in the Americas. Managing white-tailed deer requires understanding their relationship with the environment, which was characterized by Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) for all counties in Illinois, USA, who incorporated habitat quantity and quality in a deer habitat suitability index. However, this index was based on satellite imagery from 1996 and did not explore the smaller spatial scales used by deer. Our study addressed these gaps by developing a deer land cover utility (LCU) score for each TRS (township, range, and section), township, and county in Illinois based on the methodology outlined in Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) but using data from the National Land Cover Database (2001-2021). These deer LCU scores were validated against minimum deer population data using Bayesian regression with additional covariates relevant to hunting and deer density. These models performed well with Bayesian R 2 values of 0.501 (TRS), 0.5 (township), and 0.969 (county). The regression coefficients for the deer LCU scores were statistically significant (95% credibility interval not containing 0) and positive at the TRS, township, and county levels, reflecting the expected relationship between minimum deer density and deer LCU. Predictions made by these regression models on new data were accurate, with the median absolute difference between the true and predicted values being 0.398 deer/km2 for TRS', 0.085 deer/km2 for townships, and 0.066 deer/km2 for counties. This deer LCU could be used in other studies about deer in Illinois or studies in which deer are a relevant factor such as investigations about deer disease or tick distribution. This modeling approach could also be adapted to different wild species, locations, and/or time periods for which land cover data is available.

Muskrat occurrence in Rhode Island shows little evidence of land use change driving declines

AbstractMuskrat (Ondatra zibethicus) populations have been in apparent decline across their native range in North America for decades. Several hypotheses exist for the causes of these declines, including loss of wetlands. We used time‐to‐detection data from 925 surveys from 276 sites across Rhode Island, USA, between 2021–2023 to fit an occupancy model that related the probability of muskrat occupancy at a site to land cover classification. We found that muskrat occupancy was higher in areas with more open water, urban land cover, or a second‐order or larger stream, and lower in areas with salt water. We estimated changes in wetland area throughout Rhode Island using the National Land Cover Database classifications from 2001 and 2019 and found a net loss in wetland cover of 219 ha. We calculated the distance between wetland patches in each of these periods and found that patches were closer together than the dispersal distance of muskrats, suggesting isolation is unlikely to be driving muskrat declines. Additionally, when we used our model to predict changes in muskrat occupancy between 2001 and 2019, both mean and median predicted occupancy changed by <0.005. These results indicate that muskrat declines are not driven by habitat loss, and suggest future research is needed that focuses on other hypothesized mechanisms of muskrat declines such as disease, declining habitat quality, predation, and competition.

Using the National Land Cover Database as an indicator of shrub-steppe habitat: comparing two large United States federal lands with surrounding regions

ABSTRACT There is a need to assess whether ecological resources are being protected on large, federal lands. The aim of this study was to present a methodology which consistently and transparently determines whether two large Department of Energy (U.S. DOE) facilities have protected valuable ecological lands on their sites compared to the surrounding region. The National Land Cover Database (2019) was used to examine the % shrub-scrub (shrub-steppe) and other habitats on the DOE’s Hanford Site (HS, Washington) and on the Idaho National Laboratory (INL), compared to a 10-km and 30-km diameter band of land surrounding each site. On both sites, over 95% is in shrub-scrub or grassland, compared to the surrounding region. Approximately 70% of 10 km and 30-km bands around INL, and less than 50% of land surrounding HS is located in these two habitat types. INL has preserved a significantly higher % shrub/scrub habitat than HS, but INL allows grazing on 60% of its land. HS has preserved a significantly higher % grassland than INL but no grazing on site is present. The methodology presented may be used to compare key ecological habitat types such as grasslands, forest, and desert among sites in different parts of the country. This methodology enables managers, resource trustees, and the public to (1) make remediation decisions that protect resources, (2) assess whether landowners and managers have adequately characterized and protected environmental resources on their sites, and (3) whether landowners and managers have protected the integrity of that land as well as its climax vegetation.

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Urban metabolic and airway immune profiles increase the risk of infections in early childhood

BackgroundInfections in childhood remain a leading global cause of child mortality and environmental exposures seem crucial. We investigated whether urbanicity at birth was associated with the risk of infections and...

Long-term changes of agricultural land over the last century in Romania. The showcase of Romanian plain

The current paper is a space-time analysis of agricultural land transitions in the Romanian Plain, which has been significantly impacted during the past century by the fundamental structural and functional transformations. Five maps, derived from historical cartographic materials and the recent CORINE Land Cover databases, were spatially analysed using a transition matrix procedure. The aim was to identify and measure the magnitude of the main land use/cover change flows related to agricultural lands. Furthermore, several underlying factors of the spatial and functional transformations have been discussed so as to understand the evolution of the historical agricultural lands over time. The results show that the most significant changes occurred between 1912 and 1970, when 38 % of the Romanian Plain’s surface underwent transformations, with notable regional differences. The analysis of the change flows in relation to the underlying factors was instrumental in explaining the land processes, an approach that can be used to understand future landscape transformations.

Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD is organized into a spatial (raster) grid and data are available in a consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs and Grasses, there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will not be EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Assessing Spatial–Temporal Characteristics of Land Desertification from 1990 to 2020 in the Heihe River Basin Using Landsat Series Imagery

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification.

Effectiveness of Community Forest Management as an Ecosystem-Based Adaptation Strategy in the Context of Nepal

Objectives: This study attempted to explore the effectiveness of community forest (CF) management as an ecosystem based adaptation (EbA) strategy in combating and adapting with the climate change impacts upon local ecosystems and communities. Method: In order to identify EbA options, effectiveness, efficiency, sustainability, governance and socioeconomic function of CF were analyzed. Effectiveness and sustainability were examined and compared through Land Use Land Cover (LULC) database during 1996 and 2021. Efficiency was explored through the economic valuation of provisioning and cultural services, using economic approach–revealed price (market price) method. Socioeconomic functions and governance were examined through the existing plans/policies and secondary data review. Findings: CF is effectively and sustainably managing the available resources. It has increased forest cover, water body, grassland and remarkable decline in bush and sand area. Bush area (79.25 ha in 1996) dropped (to 6.52 ha in 2021) while forest cover increased from 340.46 ha to 375.54 ha. Waterbody (pond or lake area) was limited to 0.88 ha that expanded to 5.04 ha in 2021 due to the conservation oriented management practices. Meanwhile, economic value of provisioning services is USD 7182 accounting wood and timber the highest (average annual USD 4800) among the provisioning services while average annual value of firewood and poles is USD 1367 and NTFP (fish and rubber) is USD 1015 whereas the economic value of cultural services is USD 18803. Novelty: The novelty of the research work lies in the unique methodology and the selection of CF for the EbA case study, which is least explored. By exposing the integration of ecological and socio-economic considerations, this research underscores the significance CF as EbA option in shaping sustainable development pathway in Nepalese context. Keywords: Ecosystem Services, Ecosystem Based Adaptation, Community Forest, Nepal, Land Management

The Ecological Economics of Light Pollution: Impacts on Ecosystem Service Value

Light pollution has detrimental impacts on wildlife, human health, and ecosystem functions and services. This paper explores the impact of light pollution on the value of ecosystem services. We use the Simplified All-Sky Light Pollution Ratio (sALR) as a proxy for the negative impact of light pollution and the Copernicus PROBA-V Global Landcover Database as our proxy of ecosystem service value based on previously published ecosystem service values associated with a variety of landcovers. We use the sALR value to ‘degrade’ the value of ecosystem services. This results in a 40% reduction in ecosystem service value in those areas of the world with maximum levels of light pollution. Using this methodology, the estimate of the annual loss of ecosystem service value due to light pollution is USD 3.4 trillion. This represents roughly 3% of the total global value of ecosystem services and 3% of the global GDP, estimated at roughly USD 100 trillion in 2022. A summary of how these losses are distributed amongst the world’s countries and landcovers is also presented.

Hotspots of wetland loss to impervious surfaces in the conterminous United States

In this study, a first wall-to-wall comparison between the National Wetlands Inventory (NWI) and the National Land Cover Database (NLCD) was conducted across the entire conterminous United States (CONUS) to evaluate U.S. wetland loss conditions. Annually, around 26 km2 of wetlands are lost to impervious surfaces across the CONUS. Spatially, wetland loss is not evenly distributed, with 90 % of losses occurring in only 9 % of the land area, forming hotspots around expanding urban regions such as Houston, Jacksonville, and Naples. Over the past few decades, Florida experienced the highest wetland loss (5.73 km2/year) among all states, while Houston had the most wetland loss (2.54 km2/year) among all metropolitan regions. Stepwise multiple regression models identified population growth and its associated demand for new housing as the major drivers for wetland loss. Wetland loss per population increase is the highest (>15 m2/person) in most metropolitan regions around the East Coast and Gulf of Mexico. Unfortunately, current wetland loss hotspots will likely suffer further losses in future decades due to projected population growth, with Houston, Cape Coral, and Miami metropolitan regions having the greatest projected wetland loss of 89.15 km2, 34.35 km2, and 28.20 km2, respectively. This study has identified wetland loss hotspots and their drivers across the U.S. that were not possible in previous sample-based studies. The findings are critical in wetland management and protection across the U.S.

Urban Flood Modeling for Sustainability Management: Role of Design Rainfall and Land Use

This study aimed to evaluate how different methods of determining design rainfall levels and land usage affect flood hydrographs in an urban catchment; specifically, the catchment in southern Poland. The data included daily precipitation records from 1981 to 2020 and land cover information from Corine Land Cover and Urban Atlas databases for 2006 and 2018. The analysis involved examining precipitation data, determining design rainfall levels, analyzing land usage databases, exploring the influence of design rainfall levels on hydrograph characteristics, and investigating the database’s impact on these characteristics. No discernible trend in precipitation was found. The highest design rainfall values followed the GEV distribution, while the lowest followed the Gumbel distribution. Both land usage databases indicated an increasing human influence from 2006 to 2018. This study conclusively showed that the method used for estimating design rainfall and the choice of the land usage database significantly affected hydrograph characteristics. Multivariate analyses are recommended for design rainfall assessments, while the Urban Atlas database is preferred for urban catchment land usage determinations due to its detailed information.

Spatiotemporal patterns of forest pollinator diversity across the southeastern United States

AbstractAimEfforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sought to relate the diversity of bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) to ecoregion, landscape context, canopy openness, and forest composition across southeastern U.S. forests.LocationNineteen experimental forests across nine states in the southeastern U.S.MethodsWe established 5–7 plots on each experimental forest. In each, we sampled pollinators monthly (March–September) using coloured pan traps, and collected data on local forest characteristics. We used the National Land Cover Database (NLCD) to quantify surrounding landcover at different spatial scales.ResultsBee richness was negatively correlated with both the amount of conifer (pine) forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked 2 months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions.Main ConclusionsOur findings reveal ecoregional differences in pollinator communities across the southeastern U.S. and highlight the importance of landscape context and local forest conditions to this diverse fauna. The closed broadleaf forests of Appalachia and the open conifer‐dominated forests of the Coastal Plain support particularly distinct pollinator communities with contrasting seasonality. Our results suggest pine forests may reduce pollinator diversity in regions historically dominated by broadleaf forests. However, efforts to create more open canopies can help improve conditions for pollinators in planted pine forests. Research exploring associations between forest pollinators and different broadleaf tree taxa is needed to better anticipate the impacts of various management activities.

Revisiting the Landscape Mosaic model.

The landscape mosaic model quantifies and maps the spatial juxtaposition of different land uses. It provides a landscape perspective of anthropic threats posed by agriculture and urban development, and the spatial-temporal shifting of the landscape mosaic indicates landscapes where anthropic intensity has changed. We use the U.S. Geological Survey provided National Land Cover Database (NLCD) for the years 2001 and 2021 to derive the landscape mosaic at five analysis scales. To improve earlier implementations of the model, we introduce the heatmap, a flexible scheme providing more thematic reporting opportunities and allowing for better quantitative summary reporting across analysis scales as well as for temporal trends. The results are exemplified at regional scale for the Atlanta metropolitan area. We use the improved model to investigate the land cover context over time and at different analysis scales and show how custom color tables detail different thematic features of the landscape mosaic, including the degree and change of anthropic intensity. We conclude with a discussion of potential applications in ecology, landscape planning, and restoration to illustrate the benefits of the revised landscape mosaic model. All assessment tools are now available in open-source software packages.

Impact of Wildfires on Land Surface Cold Season Climate in the Northern High-Latitudes: A Study on Changes in Vegetation, Snow Dynamics, Albedo, and Radiative Forcing

Anthropogenic climate change is increasing the occurrence of wildfires, especially in northern high latitudes, leading to a shift in land surface climate. This study aims to determine the predominant climatic effects of fires in boreal forests to assess their impact on vegetation composition, surface albedo, and snow dynamics. The influence of fire-induced changes on Earth’s radiative forcing is investigated, while considering variations in burn severity and postfire vegetation structure. Six burn sites are explored in central Alaska’s boreal region, alongside six control sites, by utilizing Moderate Resolution Imaging Spectroradiometer (MODIS)-derived albedo, Leaf Area Index (LAI), snowmelt timing data, AmeriFlux radiation, National Land Cover Database (NLCD) land cover, and Monitoring Trends in Burn Severity (MTBS) data. Key findings reveal significant postfire shifts in land cover at each site, mainly from high- to low-stature vegetation. A continuous increase in postfire surface albedo and negative surface shortwave forcing was noted even after 12 years postfire, particularly during the spring and at high-severity burn areas. Results indicate that the cooling effect from increased albedo during the snow season may surpass the warming effects of earlier snowmelt. The overall climate impact of fires depends on burn severity and vegetation composition.

Abstract P115: Urban Greenspace and All-Cause, Cardiovascular Disease-Specific, and Breast Cancer-Specific Mortality Among Older Women With Breast Cancer in the United States: Regional Variations Within the Surveillance, Epidemiology, and End Results and Medicare Linked Database

Background: Cardiovascular disease (CVD) and cancer are leading causes of morbidity and mortality in the US. Cancer survivors are at increased risk for adverse CVD outcomes due to multiple factors, including cardiotoxicity. Research suggests that greenspace contributes to better health outcomes, including CVD and breast cancer (BC). Objective: To explore relationships between urban greenspace and all-cause (ACM), CVD-specific (CVDSM), and BC-specific (BCSM) mortality and explore regional differences among older women diagnosed with BC in the US SEER-Medicare database. Methods: The 2011 national landcover database was linked to the 2010 census tracts and the 2010-2017 SEER-Medicare BC cohort. Women were included if they were 66+ years at diagnosis, had invasive BC, enrolled in Medicare Part A and Part B for 12 months before diagnosis, and had a census tract match for greenspace. Independent variable ‘greenspace’ was census tract percent tree canopy quartiles. Outcomes were all-cause, CVD, and BC-specific survival. Censoring was determined by the end of the study date, 12/31/2018. Covariates included age and comorbidity at BC diagnosis, race/ethnicity, dual enrollment in both Medicare and Medicaid eligibility, neighborhood social vulnerability, and tumor stage. Results: Regional variations in association between greenspace and BC survival were observed (Table 1). Greenspace is protective for ACM and BCSM in California, Washington, New Jersey, and Michigan, while no significant associations were observed in Georgia or Louisiana. For CVDSM, greenspace is protective in CA, while no significant differences were observed in Washington, Louisiana, and Michigan. An inverse relationship is observed for CVDSM in Georgia and New Jersey. Discussion: Our findings warrant more studies to investigate why greenspace is protective in some areas (i.e., CA, NJ, MI) or hazardous in other areas (i.e., GA). There is a need to identify critical factors and pathways contributing to these greenspace-survival differences.

The landscape fragmentation: analysis of land cover transformation in high mountains environment on the example of Tatra region (southern Poland)

Changes in land cover (LC) are continuously growing due to natural and human factors, and they are even occurring within protected areas. In Europe, one such place is the Tatra region (southern Poland), which is the area of the presented research. Dynamic landscape transformation in a valuable natural area can negatively affect both environmental quality and sustainable land use planning. Appropriate recommendations and treatments can be introduced to prevent negative effects. To do this, it is necessary to understand the dynamics and character of these transformations. Therefore, the purpose of the article is to analyse changes in LC, its causes and their spatiotemporal dynamics, as well as the resulting landscape fragmentation. For this purpose, a set of landscape metrics and GIS (Geographic Information System) tools were used. The main data source in the study was the CORINE Land Cover (CLC) database. The results indicate a significant landscape transformation with a negative impact on the region’s environment, even in protected areas. Landscape fragmentation, which causes disturbances in ecosystems, increased across the entire study area. In turn, in the spatial development, it can introduce chaos and spatial disorder, and lead to the phenomenon of urban sprawl.

Sustainable Watershed Management: Assessing the Impact of Data Precision on Sediment Transport in the Oostanaula Creek Watershed, Tennessee

This study applies the WaTEM/SEDEM model, a watershed-scale model based on the Universal Soil Loss Equation (USLE), to enhance sustainable watershed management by identifying high-erosion-risk areas for targeted mitigation at various scales. It focuses on identifying potential errors in using statewide or worldwide land use data layers derived from remote-sensing algorithms and inaccuracies in the spatial distribution of cropland and soil types resulting in misinterpretation of sediment yields. These model limitations emphasize the need for field validation and precise input data, particularly stream data, to improve the reliability of these models. The study examines hydrologic rainfall-runoff processes in Tennessee’s 182 km2 Oostanaula catchment using various data sources, including the National Land Cover Database (NLCD), the European Space Agency WorldCover dataset (ESA), and manual field surveys. Three modeling scenarios were evaluated, with and without stream topology corrections, using the WaTEM/SEDEM model. It details the global data used, the methodology of the field survey, the simulation and validation of data, and the critical point identification. Significant discrepancies in long-term sediment transport predictions were found, depending on the land use data source. This study addresses watershed model validity and potential errors and recommendations for the use of globally available data.

Carbon Loss from Earthquake Induced Landslides in Fiordland

Landslides triggered by two earthquakes in Fiordland, New Zealand, were mapped using Google Earth Pro retro satellite imagery (eye altitude of 1.5-2.5 km, enabling ± 50 m precision), then catalogued and classified in a GIS inventory. Ground acceleration during the 2003 Mw 7.2 Secretary Island Earthquake resulted in at least 1852 landslides, but the larger Mw 7.8 2009 Dusky Sound Earthquake had lower accelerations and produced only 313. Both events dislodged large swaths of native forest, grassland vegetation and soil from Fiordland’s steep slopes in similar proportions, some landing in rivers and fiords. The landslide maps and magnitude-frequency distributions show close similarity to a published Global Forest Loss dataset derived from 2001-2022 satellite imagery. Assuming forest loss in this wilderness is predominantly landslide-related, it enables the more-precise earthquake-induced landslide mapping to be placed in a longer-term context of other vegetation loss, mostly rainfall-induced landslides, during the past two decades. The total area of forest loss during the 2003 earthquake was anomalous, whereas during 2009 the earthquake losses were similar to areas of forest loss assumed to be rainfall-induced landslides throughout Fiordland each year. Carbon concentrations of landslide vegetation and soil were calculated by defining vegetation types from a published Land Cover Database, and soil organic carbon concentration from a 1 km resolution raster dataset. Total carbon loss from earthquake-induced landslides amounts to 2.05E+06 tonnes for the 2003 Mw7.2 and 2.17E+05 tonnes for the 2009 Mw 7.8. By way of comparison, New Zealand’s total annual carbon sequestration was 6.3E+06 tonnes in 2020. Earthquake-induced and rainfall-induced landslides are shown to account for significant changes in stored carbon and available carbon sequestration, especially in areas of densely vegetated land, such as Fiordland. Processes of landscape disturbance should be included in carbon accounting and estimates of national greenhouse gas emissions.
 
 Supervised by: Caroline Orchiston
 Scholarship Project Funded by: Centre for Sustainability