Evaluation of Conservation Effects Assessment Project Grazing Lands conservation practices on the Cienega Creek watershed in southeast Arizona with AGWA/RHEM modeling tools

Goodrich, D. C.; Wei, Haiyan; Burns, I. Shea; Guertin, D. Phillip; Spaeth, Kenneth E.; Hernández, Mariano; Holifield-Collins, C.; Kautz, Mark A.; Heilman, Philip; Levick, Lainie R.; Ponce, Gino Iván Ayón; Carrillo, Erin; Tiller, Ronald L.

doi:10.2489/jswc.75.3.304

Cited by 7 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, these important conservation areas, and their ecological and socio-cultural values are increasingly threatened by development, invasive species, wildfire, and climate change (Bodner & Robles, 2017 ; Goodrich et al, 2020 ; Stromberg et al, 2009 ). Dewatering of Cienega Creek, through increased settlement and economic activity, has placed increasing pressure on several threatened and endangered species (Stromberg et al, 2009 ), including the Chiricahua Leopard Frog ( Lithobates chiricahuensis), Gila topminnow ( Poeciliopsis occidentalis ), Gila chub ( Gila intermedia ), longfin dace ( Agosia chrysogaster ), Southwest willow flycatcher ( Empidonax trailii extimus ), lesser long-nosed bat ( Leptonycteris yerbabuenae ), Huachuca water umbel ( Lilaeopsis schaffneriana var.…”

Section: Methodsmentioning

confidence: 99%

“…, a native shrub species that has become invasive over the past decades), pose increasing risk to the riparian ecosystem and to grassland function and health (Bodner & Robles, 2017 ). Wildfires have increased in frequency and intensity over the past decade (Goodrich et al, 2020 ). Another major concern for the watershed health is the threat of large-scale mining in the area.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Stakeholder participation, indicators, assessment, and decision-making: applying adaptive management at the watershed scale

Zúñiga-Terán

Fisher

Meixner

et al. 2022

Environ Monit Assess

View full text Add to dashboard Cite

Climate change, population growth, and declining federal budgets are threatening the health of ecosystems, and the services they provide. Under these changing conditions, managing landscapes and resources assumes new and unprecedented challenges. Adaptive management has been identified as a natural resource management approach that allows practitioners to incorporate change and uncertainty into decision-making through an iterative process that involves long-term monitoring and continued review and adjustment of management actions. However, the success of these efforts in watershed health relies on the collective and sustained monitoring of indicators, which is seldom studied. The purpose of this analysis is to examine (1) the practical challenge of choosing a list of indicators for long-term monitoring, (2) the negotiation process among stakeholders around the selection and interpretation of indicators, and (3) the communication tools that can be used to convey the assessment’s results and findings. To do this, we analyze our ongoing work in the Cienega Watershed in southern Arizona. Our analysis shows that the selective use of indicators, regular assessment and review, and establishment of partnerships among stakeholders are all important elements in establishing effective adaptive management efforts. The selection of indicators and data sources is a moving target that requires regular consensus and review among stakeholders. The assessment itself is also a powerful engagement tool with the public at large, providing legitimacy and support to land management decision-making. Here, we outline some lessons learned that can be transferred to other cases and identify potential barriers for engagement, decision-making, and project success.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Stakeholder participation, indicators, assessment, and decision-making: applying adaptive management at the watershed scale

Zúñiga-Terán

Fisher

Meixner

et al. 2022

Environ Monit Assess

View full text Add to dashboard Cite

show abstract

“…A variety of low-resolution, passive spaceborne sensors also performed effectively for their prescribed burn applications. Landsat was able to differentiate between wildfire and prescribed fire disturbance with 80% accuracy (Nguyen et al 2018), contribute to useful fuel maps for management (Franke et al 2018) and detect vegetation cover changes resulting from prescribed burns (Goodrich et al 2020). However, Landsat (along with MODIS) is a near-polar orbiting satellite, and thus provides more limited temporal coverage.…”

Section: Performance Evaluation Of Remote Sensing Products and Sensorsmentioning

confidence: 99%

Remote sensing applications for prescribed burn research

LoPresti,

Hayden,

Siegel

et al. 2024

Int. J. Wildland Fire

View full text Add to dashboard Cite

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity.

show abstract

“…Anthropogenic factors (referenced by 75 studies) are further categorised to include stocking rate, stocking density, grazing intensity, grazing system, livestock weights, mowing, soil and fire management, land use, pasture treatment (i.e., fertilizer, herbicide, nitrogen, etc.) and irrigation [2,7,49,65,66,[80][81][82][83][84][85][86][87][88][89][90]. These variables are quantified through field measurements (and/or ancillary data) and correlated against remote sensing data [50,70].…”

Section: Remotely Sensed Environmental Parameters Applied To Pasture ...mentioning

confidence: 99%

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

Ogungbuyi,

Mohammed,

Ara

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

show abstract

Evaluation of Conservation Effects Assessment Project Grazing Lands conservation practices on the Cienega Creek watershed in southeast Arizona with AGWA/RHEM modeling tools

Cited by 7 publications

References 26 publications

Stakeholder participation, indicators, assessment, and decision-making: applying adaptive management at the watershed scale

Stakeholder participation, indicators, assessment, and decision-making: applying adaptive management at the watershed scale

Remote sensing applications for prescribed burn research

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

Contact Info

Product

Resources

About