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Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1985

This land cover classification map was created using Landsat TM data from the year 1985. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.

Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1991

This land cover classification map was created using Landsat TM data from the year 1991. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.

Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1995

This land cover classification map was created using Landsat TM data from the year 1995. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.

openJan 2020View details →

Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1990

Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1990

Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1998

Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1998

opencc-by-4.0Jun 2020View details →

Multispectral and augmented Landsat data with land cover labels

Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503 The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923. The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library). The labeled classes correspond to the following identifier. 1, Temperate Coniferous forest 2, Temperate Decidius Forest 3, Temperate Mixed Forest 4, Tropical Evergreen Forest 5, Tropical Deciduous Forest 6, Scrubland 7, Wetland Vegetation 8, Agriculture 9, Grassland 10, Water body 11, Barren Land 12, Urban Area To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html). The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip. Later, a transformation to Albers equal-area conic projection was done with the followings parameters: Fake east: 2500000.0 Fake North: 0.0 Origin longitude: -102.0º Origin latitude: 12.0º First standard parallel: 17.5º Second standard parallel: 29.5º Linear unit: Meter (1.0) Reference ellipsoid: GRS80 Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation. Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized. Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated. A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class. Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected. Image patches were extracted from the selected regions in the sample. The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5. From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km. The 102 channels in the patches correspond to: Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2 Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth 2011 Landsat Time Analysis Series by Pixel (red-swir 1)/(red+swir 1); (5):    min, mean, max, std, median (red-nir)/( red+nir); (5): min, mean, max, std, median (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median (green-red)/(green+red); (5): min, mean, max, std, median (green-nir)/(green+nir); (5): min, mean, max, std, median (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median (blue-red)/(blue+red); (5): min, mean, max, std, median (blue-nir)/(blue+nir); (5): min, mean, max, std, median (blue-green)/( blue+green); (5): min, mean, max, std, median

opencc-by-4.0Jul 2020View details →

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.

opencc-by-4.0Jul 2020View details →

Raw Data of "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"

The raw data of manuscript "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"

opencc-zeroJul 2020View details →

Data from: Anthropogenic noise does not surpass land cover in explaining habitat selection of Greater Prairie-Chicken (Tympanuchus cupido)

Over the last century, increasing human populations and conversion of grassland to agriculture have had severe consequences for numbers of Greater Prairie-Chickens (Tympanuchus cupido). Understanding Greater Prairie-Chicken response to human disturbance, including the effects of anthropogenic noise and landscape modification, is vital for conserving remaining populations because these disturbances are becoming more common in grassland systems. Here, we evaluate the effect of low-frequency noise emitted from a wind energy facility on habitat selection. We used the Normalized Difference Soundscape Index, a ratio of human-generated and biological acoustic components, to determine the impact of the dominant acoustic characteristics of habitat relative to physical landscape features known to influence within-home range habitat selection. Female Greater Prairie-Chickens avoided wooded areas and row crops but showed no selection or avoidance of wind turbines based on the availability of these features across their home range. Although the acoustic environment near the wind energy facility was dominated by anthropogenic noise, our results show that acoustic habitat selection is not evident for this species. In contrast, our work highlights the need to reduce the presence of trees which have been historically absent from the region, as well as decrease the conversion of grassland to row crop agriculture. Our findings suggest physical landscape changes surpass altered acoustic environments in mediating Greater Prairie-Chicken habitat selection.

opencc-zeroAug 2021View details →

Data from: Topography, more than land cover, explains genetic diversity in a Neotropical savanna treefrog

Aim: Effective conservation policies rely on information about population genetic structure and the connectivity of remnants of suitable habitat. The interaction between natural and anthropogenic discontinuities across landscapes can uncover the relative contributions of different barriers to gene flow, with direct consequences for decision-making in conservation. Therefore, we aimed to quantify the relative roles of land cover and topographic variables on the population genetic differentiation and diversity of a stream-breeding savanna treefrog (Bokermannohyla ibitiguara) across its range. Location: Serra da Canastra mountain range, Cerrado of Minas Gerais State, Brazil. Methods: We collected and extracted DNA samples from 12 populations within and outside a strictly protected park, and used 17 microsatellite markers to assess genetic structure, among-population differentiation, and within-population diversity measures. We incorporated landscape data derived from digital models and satellite images to create connectivity matrices to correlate with genetic differentiation using Mantel tests. We used generalized linear models and path analyses to assess the roles of each landscape variable in shaping genetic diversity in this species. Results: Populations within and outside the park boundaries belonged to four genetic clusters. Most populations showed evidence of limited gene flow, with significant genetic differentiation, except for those within the park, which also had higher levels of allelic richness and heterozygosity. However, genetic differentiation among populations in this landscape was primarily explained by topographic complexity. Likewise, within-population genetic measures were best explained by models including elevation and topographic complexity, and not the amount of natural habitat or gallery forests. Main conclusions: Our results underscore that topography may be a strong historical factor shaping genetic structure among amphibian populations. Therefore, effective conservation strategies for endangered amphibians should avoid focusing exclusively on habitat suitability, and incorporate topographic complexity, which seems to be a key factor for the fauna of the extremely threatened Brazilian savanna.

opencc-zeroDec 2015View details →

Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada

Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.

opencc-zeroDec 2018View details →

Data from: Concordance in wetland physicochemical conditions, vegetation, and surrounding land cover is robust to data extraction approach

Concordance among wetland physicochemical conditions, vegetation, and surrounding land cover may result from the influence of land cover on the sources of plant propagules, on physicochemical conditions, and their subsequent determination of growing conditions. Alternatively, concordance may result if differences in climate, soils, and species pools are spatially confounded with differences in human population density and land conversion. Further, we expect that land cover within catchment boundaries will be more predictive than land cover in symmetrical buffers if runoff is a major pathway. We measured concordance between land cover, wetland vegetation and physicochemical conditions in 48 prairie pothole wetlands, controlling for inter-wetland distance. We contrasted land-cover data collected over a four-year period by multiple extraction approaches including topographically-delineated catchments and nested 30 m to 5,000 m radius buffers. After factoring out inter-wetland distance, physiochemical conditions were significantly concordant with land cover. Vegetation was not significantly concordant with land cover, though it was strongly and significantly concordant with physicochemical conditions. More, concordance was as strong when land cover was extracted from buffers <500 m in radius as from catchments, indicating the mechanism responsible is not topographically constrained. We conclude that local landscape structure does not directly influence wetland vegetation composition, but rather that vegetation depends on physicochemical conditions in the wetland (which are affected by surrounding land cover) and on regional factors such as the vegetation species pool and geographic gradients in climate, soil type, and land use.

opencc-zeroDec 2018View details →

Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range

Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.

opencc-zeroDec 2015View details →

Data from: Understanding patterns of land-cover change in the Brazilian Cerrado from 2000 to 2015

Clearing tropical vegetation impacts biodiversity, the provision of ecosystem services, and thus ultimately human welfare. We quantified changes in land cover from 2000 to 2015 across the Cerrado biome of northern Minas Gerais state, Brazil. We assessed the potential biophysical and social-economic drivers of the loss of Cerrado, natural regeneration and net cover change at the municipality level. Further, we evaluated correlations between these land change variables and indicators of human welfare. We detected extensive land cover changes in the study area, with the conversion of 23,446 km2 and the natural regeneration of 13,926 km2, resulting in a net loss of 9,520 km2. The annual net loss (-1.2% per year) of the cover of Cerrado is higher than that reported for the whole biome in similar periods. We argue that environmental and economic variables interact to underpin rates of conversion of Cerrado, most severely affecting more humid Cerrado lowlands. While rates of Cerrado regeneration are important for conservation strategies of the remaining biome, their integrity must be investigated given the likelihood of encroachment. Given the high frequency of land abandonment in tropical regions, secondary vegetation is fundamental to maintain biodiversity and ecosystem services. Finally, the impacts of Cerrado conversion on human welfare likely vary from local to regional scales, making it difficult to elaborate land use policies based solely on social-economic indicators.

opencc-by-4.0Sep 2022View details →

Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m. For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>. To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.

opencc-by-4.0Aug 2024View details →

Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model

Hcropland30:A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model ***Please note this dataset is undergoing peer review*** Version: 1.0 Authors: Qiong Hu a, 1, Zhiwen Cai b, 1, Liangzhi You c, d, Steffen Fritz e, Xinyu Zhang c, He Yin f, Haodong Weic, Jingya Yang g, Zexuan Li a, Qiangyi Yu g, Hao Wu a, Baodong Xu b *, Wenbin Wu g, * a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China b College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China c Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China d International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA e Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria f Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA <a name="_Hlk166672711"></a>g State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China   Introduction We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.   Dataset 1. Hcropland30: A hybrid 30-m global cropland map in 2020 ****Data format: GeoTiff ****Spatial resolution: 30 m ****Projection: EPSG: 4326 (WGS84) ****Values: 1 denotes cropland and 0 denotes non-cropland The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data. 2. 1°×1° Grids: This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics. ****Data format: ESRI shapefile ****Projection: EPSG: 4326 (WGS84) ****Attribute Fields: Id: The grid’s ID number. area: The area of the grid. mode: Indicates the representative sample grid. climate: The climate type the grid belongs to. dem: Average DEM value of the grid. ndvi_s1 to ndvi_s4: Average NDVI values for four seasons within the grid. esa, esri, fcs30, fromglc, glad, globeland30: Proportion of cropland pixels of different publicly available cropland products. inconsistent: Proportion of inconsistent pixels within the grid according to different public cropland products. hcropland30: Proportion of cropland pixels of our Hcropland30 dataset. 3. Samples: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021. ****Data format: ESRI shapefile ****Projection: EPSG: 4326 (WGS84) ****Attribute Fields: type: 1 denotes cropland sample and 0 denotes non-cropland sample. Citation If you use this dataset, please cite the following paper: Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted. License The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Disclaimer This dataset is provided as-is, without any warranty, express or implied. The dataset author is not responsible for any errors or omissions in the data, or for any consequences arising from the use of the data. Contact If you have any questions or feedback regarding the dataset, please contact the dataset author Qiong Hu (huqiong@ccnu.edu.cn)

opencc-by-4.0Sep 2024View details →

Modified land cover georeference data based on ESA Worldcover 2021

Open the record for dataset details and reuse information.

opencc-zeroJun 2021View details →

Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees

<a name="_Hlk64216363">Understanding habitat quality is central to understanding the distributions of species on the landscape, as well as to conserving and restoring at-risk species. Although it is well-known that many species require different resources throughout their life cycles, pollinator conservation efforts focus almost exclusively on forage resources. In this study, we evaluate nesting habitat for bumble bees by locating nests directly on the landscape. We compared colony density and colony reproductive output for Bombus impatiens, the common eastern bumble bee, across three different land cover types (hay fields, meadows, and forests). We also assessed nesting habitat associations for all Bombus nests located during surveys to tease apart species-specific patterns of habitat use. We found that B. impatiens nested under the ground in two natural land cover types, forests and meadows, but found no B. impatiens nests in hay fields. Though B. impatiens nested at similar densities in both meadows and forests, colonies in forests had much higher reproductive output. </a>In contrast, B. griseocollis tended to nest on the surface of the ground and was almost always found in meadows. B. perplexis was the only species to nest in all three habitat types, including hay fields. For some bumble bee species in this system, meadows, the habitat type with abundant forage resources, may be sufficient to maintain them throughout their life cycles. However, B. impatiens might benefit from heterogeneous landscapes with forests and meadows. Results for B. impatiens emphasize the longstanding notion that habitat use is not always positively correlated with habitat quality (as measured by reproductive output). Our results also show that habitat selection by bumble bees at one spatial scale may be influenced by resources at other scales. Finally, we demonstrate the feasibility of direct nest searches for understanding bumble bee distribution and ecology.

opencc-zeroAug 2021View details →

Greater Prairie-chicken data used in "Responses to land cover and grassland management vary across life-history stages for a grassland specialist"

Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater Prairie-Chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007-2009 and 2014-2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of Greater Prairie-Chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie-chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15-20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life history stages, and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for Greater Prairie-Chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie-chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.