Spatial Analysis For Conservation and Sustainability
Land Use
Land use change is currently the largest threat to biodiversity, and exacerbates the detrimental effects of climate change. We are interested in novel types of land use change, such as housing growth in the WUI, and widespread land abandonment after socioeconomic shocks, and how such changes affect biodiversity.
Closing the research-implementation gap is key for advancing biodiversity conservation. One approach is to generate ecologically relevant spatial datasets that integrate easily with existing management plans. Our goal was to identify priority forest conservation areas in Argentina by combining species distributions, human footprint data, and existing forest zoning. We: (i) mapped potential habitat distributions of 70 plant and animal species associated with forests, and of recognized social and ecological importance, (ii) combined the species distributions with human footprint data to identify priority conservation areas, and (iii) evaluated the juxtaposition of our priority conservation areas with current forest management zones. We found that priority conservation areas (i.e., high number of species and low human footprint) are poorly protected by the current zoning scheme. While the Andean-Patagonian region had a substantial portion (57 %) of priority conservation areas in high protection zones, in four other forest regions we evaluated, only 16–37 % of priority areas had high protection levels. Of great concern are the Chaco and Espinal regions, where 36 % and 39 %, respectively, of priority conservation areas are in low protection zones, where conversion to other uses (row crops, livestock) is allowed. Our results provide new spatial information to managers and conservationists highlighting where current forest zoning performs well, and where it may warrant re-evaluation. Overall, our study highlights the value of integrating species distributions and human footprint maps into existing land use plans to guide conservation efforts in data-poor countries, and is an example of a strategy for closing the research-implementation gap.
Human activity is recognized as a major driver of changes in land cover, land use, and fire regimes that influence and disrupt ecosystems. The Wildland-Urban Interface (WUI) defines areas where low-density settlements overlap with high amounts of wildland vegetation cover and are a focal point for wildfire hazard risks. WUI land uses are expanding and cause many biotic and abiotic implications for the environment and ecosystem functionality, such as declining biodiversity, loss, and fragmentation of habitat. Therefore, we require spatial and temporal information to investigate the rate and extent of the WUI growth and assess its structure and composition for preventing wildfire hazard risk and monitoring its impacts on ecosystems. Point-based WUI maps for South Australia, California (Carlson et al 2022), and Portugal. WUI reference datasets: Australia: Microsoft Building Footprint (~2018) & DEA Land Cover 2018, California: Microsoft Building Footprint (~2015) & NLCD (2016), Portugal: Microsoft Building Footprint (2014-2021) & COS Land Cover (2018).
Especially in the Mediterranean-climate biome, fire has an important ecological role, which put humans living within the Wildland-Urban Interface at high risk, and fire hazards in these regions have achieved media attention in recent years. Mapping the WUI is essential to quantify the extent of this increasing settlement in wildland vegetation-dominated areas. Standard land cover maps often do not include this widespread settlement type, while differentiating only either into high-density urban land uses or vegetation cover.
So far, the extent of the Wildland-Urban Interface and its growth has only been mapped for a few countries or regions based on national land cover products and census or build-location datasets. However, these datasets are only limited available, and therefore, limit our ability to consistently capture WUI area growth, its structures, and drivers. Wildfire in the close proximity of Adelaide (Australia) in 2020 (Landsat-8 from 2nd January 2020, RGB-vizualization: NIR-RED-GREEN, source USGS)
Kira Pfoch is conducting a study aiming at filling this gap in data availability. Kira’s main objective is to map WUI growth within the Mediterranean-climate biome with the Landsat Archive. She investigates reasons for WUI growth that is likely to be associated with expanding human activities that lead to increasing settlements in natural landscapes. Kira further investigates the relation between WUI growth and changes in wildfire activities and characteristics, to assess whether increasing human influences affect fire activity within these landscapes. Finally, the impact and interaction of human activity, climate change, and wildfires affect the natural vegetation, and therefore, Kira studies vegetation type conversion concerning WUI growth across different regions within the Mediterranean climate biome.
In conclusion, Kira’s research will help determine to identify growing human-environment conflict and its impacts on fire and vegetation. Wildfire activity in Portugal in 2017 ((left image) pre-fire image 10th July 2017, (right image) fire image from the 26th July 2017, source USGS)
Landscapes are undergoing continuous transformation, with both natural and human factors causing the destruction of some habitats and the formation of others. While wildlife can adapt to natural changes, the current scale of human-made landscape alterations is much greater than nature’s ability to adapt. Some species can thrive in human-made landscapes, but others are at risk of extinction due to habitat loss.
Azerbaijan, a country in the Caucasus region with rich biodiversity and a long history of human-driven land cover changes. For conservation and sustainable management there, it is critical to understand the impacts of landscape changes on wildlife habitats. The changes in the Caucasus eco-region have accelerated in the 20th century due to population growth and Soviet nature-transformation efforts. A new study by Afag Rizayeva aims to understand the impact of these changes on the habitats of eight ungulate species, including common animals like wild boar and roe deer, as well as a rare species of gazelle. With landscapes constantly changing due to natural and human causes, it is increasingly important to understand how these changes are affecting wildlife populations. East Caucasian tur – Capra cylindricornis by Azerchin Muradov
Afag has developed the Caucasus land cover maps for the 1960s using former spy satellite images (Corona) and has analyzed the long-term changes in these landscapes using recent land cover maps derived from Landsat images. Her research begins by using the presence data of eight ungulate species, conducting species distribution modeling to evaluate their current ranges and the landscape features that are most important to each species. Red deer – Cervus elaphus by Azerchin Muradov
Next, Afag will analyze the changes in land cover within each species’ range, determining stable areas, habitat gains and losses, and assessing the positive or negative effects of these changes on the species’ habitats. This will enable her to determine if the species can continue to use the same areas despite human activities, or if they require urgent land management solutions to protect them. The results of Afag’s study will help guide wildlife conservation planning and will be used by local NGOs and government agencies. As human impact on nature is a global issue, the methodological approach she develops in her research will have applications in other regions facing similar issues. Chamois – Rupicapra rupicapra by Azerchin Muradov
In conclusion, understanding the impact of long-term land cover changes on wildlife habitats is crucial for conservation and sustainable management. The research being conducted by Afag Rizayeva will provide valuable insights into this issue and help guide efforts to protect the wild species in Azerbaijan and beyond. Stay tuned!
The Wildland-Urban Interface (WUI) is where houses and wildland vegetation meet or intermingle. This makes that area a place of potentially severe human-environmental conflict, where settlements are exposed to wildfire, habitat is fragmented, and the risk of spreading zoonotic diseases is increased. While there is evidence for WUI across Australia, Europe, and North America, the worldwide distribution of the WUI is yet unknown.
A group of SILVIS researchers and external collaborators recently presented a global map of the 2020 WUI at 10 m resolution using a globally consistent approach based on remote sensing-derived datasets of building area and wildland vegetation, and identified hotspots of wildfire hazard in the WUI. The total global WUI area in 2020 was 6.4 million km2, or 4.9% of global land area, which is about twice the size of India. Globally, 3.3 billion people live in the WUI, of which two thirds live in landscapes dominated by forests, shrubland, and wetland, and one third in landscapes dominated by grassland. While WUI hotspots could be identified across all continents, Europe and North America have the highest land area share of WUI (12% and 6%). Patterns are highly diverse on a country level with regard to WUI land area share, population living in the WUI and dominant land cover types (Fig. 1). Fig. 1: Wildland-Urban Interface area and population share by world region (top) and for selected countries (bottom). Some US states were also included for better comparability to previous research. F/S/W: Forest/Shrubland/Wetland, Grass.: Grassland.
Wildfires are of increasing concern across the globe, as their frequency, intensity and season-length has increased due to various factors, including climate change, more human ignitions, and rising fuel loads. Wildfires are a particular problem in the WUI, as they cause substantial losses of homes and lives there. Indeed, nearly two thirds of all people affected by wildfires since 2001 lives in the WUI, while only a small share (2% – 10% across regions) of all global wildfire occurrences in that period were in the WUI. Differences among world regions and countries persist here. In North America, 85% of the population affected by wildfire lives in the WUI, but in Africa only 50% does (Fig. 2). Fig. 2: Wildfire and the Wildland-Urban Interface (WUI). A: Share of area affected by wildfire within and outside of the WUI. B: Share of population that experienced wildfire within 1 km of their home within and outside of the WUI. Africa (AF), Asia (AS), Europe (EU), North America (NA), Oceania (OC), and South America (SA).
As fire-prone areas expand globally, a global perspective on WUI data can help guide proactive actions to prepare for future wildfire in the WUI. This is particularly useful in areas where there is a high probability of becoming areas of increased fire hazard towards the middle of the 21st century, such as Temperate Broadleaf and Mixed Forests and (Sub-Tropical) Moist Broadleaf Forests, two biomes that represent 61% of the global population. The global Wildland-Urban Interface. Percent area of the Wildland-Urban Interface (WUI) in ca. 2020 per hexagon with 50 km diagonal length.
Wild forests – forests where human influence levels are low or null – provide important habitat for plants and animal, and therefore are a top priority for conservation. Yet forests around the world are being lost and degraded at high rates, and with this the remaining wildest forests. This is the case for Argentina, in southern South America, which supports diverse forest ecosystems but also high rates of forest loss (Figure 1). Figure 1. Argentina supports different forest regions that are a result of large latitudinal and elevational gradients. Photos by N. Polity and G. Martínez Pastur
With an international team of US (Silvis Lab) and Argentinian researchers (National Scientific and Technical Research Council, and National Parks Administration), and with funding from NASA, I mapped the human footprint in Argentina’s forested areas to help conservation planning at regional and country levels.
The human footprint is a mapping approach that combines data on roads, human settlements, power lines, and other anthropogenic threats, into a single index. The assumption is that forests that are far away from these human features are likely to have low or null human influence, and thus represent potential wild forests (Figure 2). However, until now, such information was unavailable for conservation planners in Argentina, or was too coarse to be useful. Figure 2. Roads, human settlements, and agriculture are major threats to forests in Argentina. Their ecological impact expands hundreds of meters inside the forest. Image source: Google Earth.
Our human footprint map shows that a substantial portion (43%) of Argentina’s forests remain wild, which suggests there are unique opportunities for conservation. However, we found that the level of human influence varied across the county, and Atlantic and Chaco forests, both in northern Argentina, have the highest levels of human influence (Figure 3). Figure 3. Human footprint index in the native forest areas of Argentina developed by this stufy. 1. Yungas/Chaco, 2. Atlantic, 3. Espinal, and 4. Andean-Patagonian Forests
Our study revealed that Argentina’s wildest forests are under threat. In Argentina, land use in forested areas is regulated by law, which dictates which areas can be deforested, which areas should be protected, and which can be used for activities thought to be sustainable (like silvopasture). Unfortunately, we found that most (78%) of the wild forests are in places where allowed activities can threaten the ecological integrity of these forests, diminishing their biodiversity conservation value.
Our study provides new datasets for forest conservation planning in Argentina, and highlights the urgent need to strengthen protection of the remaining wildest forests. The human footprint map developed in this study can be used for a variety of purposes related to forest conservation, such as refining the types of activities allowed in forest areas, planning for new protected areas (national parks or provincial reserves), identification of ecological corridors, and promotion of payments for private owners to maintain the intactness of wild areas, among others.
The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change.
Development in natural areas is a leading threat to biodiversity. Global conservationists have called for the expansion of protected areas to preserve wildlands that are free from buildings, and in the U.S., the ‘America the Beautiful’ initiative aims to protect 30% of land and water areas by 2030 (known as the ‘30x30’ target). Here, we determined opportunities and limitations for conservation in the conterminous U.S. by assessing the extent of buildings in wildland vegetation. We focused specifically on National Forest lands, as these contain numerous private inholdings where development may occur. Using a newly available building footprint dataset, we determined 1) whether buildings were present and 2) numbers of buildings within three distances of wildland vegetation (100, 250, and 500 m), representing varying magnitudes of ecological impact. Our findings revealed that 29% of wildland vegetation nationwide was within 500 m of a building, 15% was within 250 m, and 5% was within 100 m. National Forest lands were less affected by building disturbance, but a substantial proportion (12%) of wildland vegetation area was within 500 m of a building. Of National Forest lands that were within 500 m of an inholding, 76% was not yet in proximity to a building; consequently, ~10% of National Forest lands (143,474 km2) are susceptible to impacts from future development on inholdings. We conclude that National Forest inholdings are therefore important opportunity areas for 30x30 conservation goals. Our assessments can inform where conservation efforts can limit impacts from present and future development on biodiversity.
Context: The wildland-urban interface (WUI) is an area where houses are located near wildland vegetation. As such, the WUI is a focal area of wildfre risk, human-wildlife conficts, and other human-nature interactions. Although there is a wide consensus on the impact WUI existence might have, little is known about the WUI spatial determinants over long periods, especially in countries with long settlement history.
Objectives: Our goal here was to map the WUI across Poland, and to quantify the extent to which historical legacies shape current WUI pattern, since Poland is one of the countries, which experienced substantial political changes over time, which had an impact on historical settlement development.
Methods: We analysed a database of nearly 15 million building locations and a 10-m Sentinel-2-based land cover map to produce a country-wide WUI map of Poland. Then we compared the WUI pattern among parts of Poland which belonged to diferent political entities in 1900s and 1930s and also among diferent ecoregions. Lastly, we verifed the efects of the historical borders or landscape units borders on WUI patterns with a discontinuity analysis.
Results: We found that a substantial part of Poland is WUI, and over 60% of all buildings are in WUI. However, WUI patterns difer considerably across the country, and WUI hotspots are located around the largest metropolitan areas in central and southern part of Poland and in the Carpathians. Furthermore, WUI patterns refect pre-1945 national borders indicating long-term legacies of past settlement patterns and urban planning approaches. Diversity among ecoregions was much less pronounced than among past political entities.
Conclusions: Our work shows that current WUI pattern is to large extent shaped by former political conditions, which is likely true not only in Poland, but also in many parts of Europe and elsewhere where settlement history goes back centuries.
Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.
Historical land use strongly influences current landscapes and ecosystems making maps of historical land cover an important reference point. However, the earliest satellite-based land cover maps typically date back to the 1980s only, after 30-m Landsat data became available. Our goal was to develop a methodology to automatically map land cover for large areas using high-resolution panchromatic Corona spy satellite imagery for 1964. Specifically, we a) conducted a comprehensive analysis on the feature selection and parameter setting for largearea classification processes for 2.5-m historical panchromatic Corona imagery for a full suite of land cover classes, b) compared the pixel-based and object-oriented methods of classifying the land cover, and c) examined the benefits of adding a digital elevation model for the pixel-based and object-oriented land cover classifications. We mapped land cover in parts of the Caucasus Mountains (158,000 km2), a study area with great variability in land cover types and illumination conditions. The overall accuracies of our pixel-based and object-oriented land cover maps were 63.0 ± 5.0% and 67.3 ± 4.0%, respectively, showing that object-oriented classifications performed better for Corona satellite data. Incorporating the digital elevation model improved the overall accuracy to 75.3 ± 3.0% and 78.7 ± 2.5%, respectively. The digital elevation model was especially useful for differentiating forest and snow-and-ice from lakes in mountainous areas affected by cast shadows. Our results highlight the feasibility of accurately and automatically classifying land cover for large areas based on Corona spy satellite imagery for the 1960s. Such land cover maps predate the earliest 30-m Landsat land cover classifications by two decades, and those from high-resolution satellite imagery by four decades. As such, we demonstrate here that Corona imagery can make important contributions to global change science.
