Geology topics

Zhiqiang Yang

Publications and source records attributed to Zhiqiang Yang.

7 recordsLinked to original sources

USGS2026

Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird

1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood. 2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet ( Brachyramphus marmoratus )—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions. 3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change. 4. Synthesis and applications : We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.

California, Oregon, Washington↗

USGS2020

Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as "duplicate interpretations"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.

Remote Sensing of Environment↗

USGS2020

A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest

This guidebook provides user-friendly overviews of a variety of spatial datasets relevant to conservation and management of natural resources in the face of climate change in the Pacific Northwest, United States. Each guidebook chapter was created using a standardized template to summarize a spatial dataset or a group of closely related datasets. Datasets were selected according to standardized criteria based on input through a collaborative process involving researchers and natural-resource managers throughout the Pacific Northwest region. In each chapter, basic spatial and temporal information is provided for the dataset, along with a conceptual overview, glossary of key terms, links to download data and supporting documentation, a brief methods summary describing how the dataset was created, guidelines for dataset interpretation, assessment of uncertainties along with evaluation of caveats and simplifying assumptions, and information about potential and actual conservation applications of the dataset. Collectively, this information provides natural-resource managers with “snapshots” of a variety of datasets representing diverse processes and conditions, including climate projections, changes in hydrologic conditions, vegetation and fire-regime shifts, animal habitat changes, species movements, and topographic and soil conditions relevant to climate change. Along with other types of data and site-specific information, the datasets described in this guidebook have the potential to inform management of valued natural resources throughout the Pacific Northwest region in the context of adaptation to changing climate conditions.

Idaho, Montana, Oregon, Washington↗

USGS2019

Social status, forest disturbance, and Barred Owls shape long-term trends in breeding dispersal distance of Northern Spotted Owls

Dispersal among breeding sites in territorial animals (i.e. breeding dispersal) is driven by numerous selection pressures, including competition and spatiotemporal variation in habitat quality. The scale and trend of dispersal movements over time may signal changing conditions within the population or on the landscape. We examined 2,158 breeding dispersal events from 694 male and 608 female individually marked Northern Spotted Owls ( Strix occidentalis caurina ) monitored over 28 yr on 7 study areas to assess the relative importance of individual (sex, experience), reproductive (annual productivity, mate availability), and environmental (forest alteration, presence of competitor) sources of variation in breeding dispersal distance. Median breeding dispersal distance was 3.17 km, with 99% of all breeding dispersal events <37 km. Mean annual dispersal distances increased by 2.43 km in Oregon and 9.40 km in Washington between 1990 and 2017, which coincided with increases in annual detections of nonnative Barred Owl ( S. varia ). Frequency of breeding dispersal events, both among and within individuals, also increased over time. Female owls moved farther than males (median of 3.26 and 3.10 km, respectively), and birds with less experience (territory tenure) moved farther than those with more experience. Owls that were single in the year prior to dispersal moved 13–31% farther than those paired prior to dispersal. The greatest environmental change occurring over the course of our study was the expansion of Barred Owl populations. Breeding dispersal distance was positively related to Barred Owls in the study area and disturbance within the originating territory. While it appears that social factors continue to be important drivers of breeding dispersal distance in Spotted Owls, increased competition from Barred Owls and habitat alteration have a contributing effect. Increased breeding dispersal distances should be of concern for conservation efforts and considered in population monitoring because changing dispersal behavior may lead to higher rates of mortality and/or emigration from historical study areas.

Oregon, Washington↗

USGS2018

Mapping forest change using stacked generalization: An ensemble approach

The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.

Remote Sensing of Environment↗

USGS2017

How similar are forest disturbance maps derived from different Landsat time series algorithms?

Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.

Forests↗

USGS2017

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Environmental Monitoring and Assessment↗