Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Jansen, Vincent; Kolden, Crystal A.; Taylor, R B; Newingham, Beth A.

doi:10.1080/01431161.2015.1117681

Cited by 19 publications

(14 citation statements)

References 69 publications

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“…Jansen [27] investigated the quantification of livestock effects on the scalable, season specific metric of Landsat imagery and biomass identification and development of a model assessing spatial relationships between spectral indices and ruminants over a growing season. The focus was on finding significant correlations between existing biomass, vegetation metrics and management practices to quantify changes in vegetation due to grazing.…”

Section: Discussionmentioning

confidence: 99%

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Colin¹,

Clifford²,

Wu³

et al. 2017

OJS

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Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.

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Section: Discussionmentioning

confidence: 99%

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Colin¹,

Clifford²,

Wu³

et al. 2017

OJS

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“…The sample site locations were located using a stratified random sampling approach generated iteratively for each season and year. Sampling strata were divided by quartiles of predicted biomass amounts derived initially using vegetation models created for this study area [21] and then subsequently updated as new data were collected and analyzed for each season. This stratification was performed for more informed and efficient sampling across a gradient of vegetation amounts.…”

Section: Sampling Designmentioning

confidence: 99%

“…Using a best-subset regression modeling approach [21,46], we determined which spectral indices (Supplemental Materials Table S2) were most commonly selected for a defined number of variables [46] when estimating vegetation biomass and cover for nine different data combinations based on the sensor and time of year (sensor-time). The best-subset approach exhaustively searches all possible single and multiple variable linear models (with model size defined by the user) and selects the models with the best fit [46].…”

Section: Variable Selectionmentioning

confidence: 99%

“…Due to the impact that vegetation phenology has on accurately estimating vegetation amounts with remotely sensed data [21,53,54] and the desire to automate the application of the appropriate phenological models across the grazing season, we estimated vegetation amounts using an 'if-else' statement, whereby if the sampling site value of the NDVI is greater than x, apply the summer model, else apply the fall model. To determine the threshold for each metric (biomass or cover) and sensor, we created a sequential range of NDVI values from 0.25 to 0.60 with a 0.01 step and applied our 'if-else' statement to predict a vegetation amount for each site.…”

Section: Exploring Pixel-wise Phenology-driven Model Applicationmentioning

confidence: 99%

“…While previous research in rangeland systems highlights the ability of remotely sensed data to assess vegetation over time or monitor grazing effects, ranchers and others who must adapt their management to changing conditions still need easily accessible, spatiotemporally consistent data products. For example, management decisions may depend on multiple estimates of vegetation across the grazing season [21] or on residual vegetation amounts after vegetation has senesced (e.g., [30,32]). New developments in the automatic post-processing of raw remote sensing data (e.g., Landsat CDR surface reflectance products) and cloud computing and storage makes this increasingly achievable.…”

Section: Introductionmentioning

confidence: 99%

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The Development of Near Real-Time Biomass and Cover Estimates for Adaptive Rangeland Management Using Landsat 7 and Landsat 8 Surface Reflectance Products

2018

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Rangelands are critical working landscapes and are the focus of considerable conservation planning efforts globally. A key conservation challenge in these landscapes is that high interannual variability in both climatic conditions and land use greatly limits the utility of outdated or static vegetation maps for management decision-making. One potential solution to this problem lies in remote sensing-derived information; however, prospective users must have continuous and timely access to vegetation products tailored to their needs. Google Earth Engine (GEE) can overcome the many storage, processing, and visualization barriers associated with creating ready-to-use remote sensing products for the public. While GEE provides a platform for building tools to analyze data and share results with users in near real-time for adaptive management, monitoring products need to (1) provide accurate and stable estimates over time and (2) align with management goals and the ecology of the rangeland system in question. Here, we assess estimates of vegetation cover and above-ground biomass at two dominant phenological time periods (summer/green and fall/brown), as modeled from the Landsat 7 and Landsat 8 Climatic Data Record (CDR) product. Using a best-subset regression modeling approach, we modeled vegetation cover and biomass, finding that the best predictors vary by season, corresponding to vegetation phenology. We also found that sensor-specific models decreased the relative differences between mapped cover and biomass estimates when comparing Landsat 7 and Landsat 8 scenes one day apart in the summer and fall. Ultimately, we developed an automated model selection process driven by sensor and vegetation greenness that can run in GEE to monitor and analyze vegetation amounts across the grazing season for adaptive management.

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Modeling proximate causes of deforestation in Antioquia, Colombia

Calderón-Caro,

Morales-Gómez,

Gutiérrez-Vélez

et al. 2024

Reg Environ Change

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Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Cited by 19 publications

References 69 publications

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

The Development of Near Real-Time Biomass and Cover Estimates for Adaptive Rangeland Management Using Landsat 7 and Landsat 8 Surface Reflectance Products

Modeling proximate causes of deforestation in Antioquia, Colombia

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