Key Areas For Conserving United States\u27 Biodiversity Likely Threatened By Future Land Use Change

A major challenge for biodiversity conservation is to mitigate the effects of future environmental change, such as land use, in important areas for biodiversity conservation. In the United States, recent conservation efforts by The Nature Conservancy and partners have identified and mapped the nation\u27s Areas of Biodiversity Significance (ABS), representing the best remaining habitats for the full diversity of native species and ecosystems, and thus the most important and suitable areas for the conservation of native biodiversity. Our goal was to understand the potential consequences of future land use changes on the nation\u27s ABS, and identify regions where ABS are likely to be threatened due to future land use expansion. For this, we used an econometric-based model to forecast land use changes between 2001 and 2051 across the conterminous U. S. under alternative scenarios of future land use change. Our model predicted a total of similar to 100,000 to 160,000 km(2) of natural habitats within ABS replaced by urban, crop and pasture expansion depending on the scenario (5% to 8% habitat loss across the conterminous U.S.), with some regions experiencing up to 30% habitat loss. The majority of the most threatened ABS were located in the Eastern half of the country. Results for our different scenarios were generally fairly consistent, but some regions exhibited notable difference from the baseline under specific policies and changes in commodity prices. Overall, our study suggests that key areas for conserving United States\u27 biodiversity are likely threatened by future land use change, and efforts trying to preserve the ecological and conservation values of ABS will need to address the potential intensification of human land uses

Current And Future Land Use Around A Nationwide Protected Area Network

Land-use change around protected areas can reduce their effective size and limit their ability to conserve biodiversity because land-use change alters ecological processes and the ability of organisms to move freely among protected areas. The goal of our analysis was to inform conservation planning efforts for a nationwide network of protected lands by predicting future land use change. We evaluated the relative effect of three economic policy scenarios on land use surrounding the U.S. Fish and Wildlife Service\u27s National Wildlife Refuges. We predicted changes for three land-use classes (forest/range, crop/pasture, and urban) by 2051. Our results showed an increase in forest/range lands (by 1.9% to 4.7% depending on the scenario), a decrease in crop/pasture between 15.2% and 23.1%, and a substantial increase in urban land use between 28.5% and 57.0%. The magnitude of land-use change differed strongly among different USFWS administrative regions, with the most change in the Upper Midwestern US (approximately 30%), and the Southeastern and Northeastern US (25%), and the rest of the U.S. between 15 and 20%. Among our scenarios, changes in land use were similar, with the exception of our restricted-urban-growth\u27\u27 scenario, which resulted in noticeably different rates of change. This demonstrates that it will likely be difficult to influence land-use change patterns with national policies and that understanding regional land-use dynamics is critical for effective management and planning of protected lands throughout the U.S

Economic-based Projections Of Future Land Use In The Conterminous United States Under Alternative Policy Scenarios

The article presents a study which constructs and parameterizes an econometric model of land-use change to project future land use to the year 2051 at a fine spatial scale across the conterminous U.S. under several alternative land-use policy scenarios. It parameterizes the econometric model of land-use change with the National Resource Inventory (NRI) 1992 and 1997 land-use data for 844 000 sample points

Current and Future Land Use around a Nationwide Protected Area Network

Land-use change around protected areas can reduce their effective size and limit their ability to conserve biodiversity because land-use change alters ecological processes and the ability of organisms to move freely among protected areas. The goal of our analysis was to inform conservation planning efforts for a nationwide network of protected lands by predicting future land use change. We evaluated the relative effect of three economic policy scenarios on land use surrounding the U.S. Fish and Wildlife Service’s National Wildlife Refuges. We predicted changes for three land-use classes (forest/range, crop/pasture, and urban) by 2051. Our results showed an increase in forest/range lands (by 1.9% to 4.7% depending on the scenario), a decrease in crop/pasture between 15.2% and 23.1%, and a substantial increase in urban land use between 28.5% and 57.0%. The magnitude of land-use change differed strongly among different USFWS administrative regions, with the most change in the Upper Midwestern US (approximately 30%), and the Southeastern and Northeastern US (25%), and the rest of the U.S. between 15 and 20%. Among our scenarios, changes in land use were similar, with the exception of our ‘‘restricted-urban-growth’’ scenario, which resulted in noticeably different rates of change. This demonstrates that it will likely be difficult to influence land-use change patterns with national policies and that understanding regional land-use dynamics is critical for effective management and planning of protected lands throughout the U.S

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world’s most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR-derived measures of three-dimensional canopy structure and subcanopy topography using classification-tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDARbased results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar-based topographic data. The accuracy of the LiDAR-based forest type classification (including native- and exotic-dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR-derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR-derived variables of vegetation structure were better predictors of forest successional status (i.e., mid-secondary, late-secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification-tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general

Awesome Spectral Indices

Awesome Spectral Indices is a standardized machine-readable catalogue of spectral indices for remote sensing in Earth system research. Currently, the catalogue has 228 spectral indices grouped in 7 application domains: vegetation, water, burn, snow, urban, radar, and kernel indices. Note that radar and kernel indices represent methodological approaches. Each index in the catalogue consists of an item with 9 attributes, listed as follows: short_name: Short name (acronym) of the index. long_name: Long name (original name) of the index. application_domain: Application domain of the index (one of the 7 above-mentioned groups). formula: Formula of the index given as a standardized expression. bands: Required bands and additional parameters for the index computation. platforms: List of platforms with the required bands for the index computation. reference: Link to the index source. date_of_addition: Date of addition to the catalogue. contributor: GitHub user link of the index contributor. The catalogue is released in two formats: JSON and CSV. The JSON file follows a key-value model with the index acronym as key and the 9 attributes as value. The CSV file follows a relational model with indices as rows and the 9 attributes as columns. The two filenames are: spectral-indices-dict.json (JSON file) spectral-indices-table.csv (CSV file) For a complete and detailed description, please see github.com/awesome-spectral-indices/awesome-spectral-indices. The dynamic GitHub repository includes the source code used to create the catalogue

QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon

Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO{sub 2}) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across {approx}20,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of >70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems

Baseline greenhouse gas emissions and removals for forest and rangelands in Arizona

The project in this report sought to quantify the baseline of changes in carbon stocks on forest and agricultural lands in Arizona for the 1990s. These baselines provide an estimate of the emissions and removals of greenhouse gases attributable to changes in the use and management of land and are useful for identifying where major opportunities could exist in Arizona for enhancing carbon stocks and/or reducing carbon sources to potentially reduce greenhouse gas emissions.   The analysis revealed that forests were responsible for a net removal of carbon dioxide from the atmosphere of 0.9 million metric tons of carbon dioxide per year (MMTCO2/yr) between 1987 and 1997, and that agricultural lands were responsible for a net emission of 0.04 MMTCO2/yr. On non‐federal lands emissions from forests caused by development were estimated at 0.0145– 0.0152 MMTCO2/yr, and between 1990 and 1996 154,000 acres of forest and rangeland were burned by fires with an estimated emission of 0.47 MMTCO2eq/yr. Nitrous oxide (N2O) and methane (CH4) emissions (in CO2 eq) from agricultural lands are more than 100 times higher than carbon emission due to land‐use change.&nbsp

Single species conservation as an umbrella for management of landscape threats

Single species conservation unites disparate partners for the conservation of one species. However, there are widespread concerns that single species conservation biases conservation efforts towards charismatic species at the expense of others. Here we investigate the extent to which sage grouse (Centrocercus sp.) conservation, the largest public-private conservation effort for a single species in the US, provides protections for other species from localized and landscape-scale threats. We compared the coverage provided by sage grouse Priority Areas for Conservation (PACs) to 81 sagebrush-associated vertebrate species distributions with potential coverage under multi-species conservation prioritization generated using the decision support tool Zonation. PACs. We found that the current PAC prioritization approach was not statistically different from a diversity-based prioritization approach and covers 23.3% of the landscape, and 24.8%, on average, of the habitat of the 81 species. The proportion of each species distribution at risk was lower inside PACs as compared to the region as a whole, even without management (land use change 30% lower, cheatgrass invasion 19% lower). Whether or not bias away from threat represents the most efficient use of conservation effort is a matter of considerable debate, though may be pragmatic in this landscape where capacity to address these threats is limited. The approach outlined here can be used to evaluate biological equitability of protections provided by flagship species in other settings