Application of Ecological Classification and Predictive Vegetation Modeling to Broad-Level Assessments of Ecosystem Health

Jensen, Mark E.; Redmond, Roland L.; Dibenedetto, Jeff P.; Bourgeron, Patrick S.; Goodman, Iris A.

doi:10.1007/978-94-011-4343-1_17

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…The variability that visual assessments display in relation to specific condition parameters and vegetation types suggests that they should not be used for high‐resolution management decisions, which require details about individual condition parameters. There is a real need for cost‐effective condition information to inform management decisions (Jensen et al. 2000), and for methods that provide sufficiently rigorous data.…”

Section: Resultsmentioning

confidence: 99%

“…2005; USDA 2003), (iv) inform strategic management planning (NSW DEC 2005) and (v) report biodiversity conservation progress (CBD 2004). While a useful planning tool for biodiversity conservation, the costs of vegetation condition assessment methods can be prohibitively high; hence, there is an ongoing need for accurate and cost‐effective on‐ground vegetation condition assessments (Jensen et al. 2000; Beck & Gessler 2008).…”

Section: Introductionmentioning

confidence: 99%

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Is what you see what you get? Visual vs. measured assessments of vegetation condition

Cook

Wardell‐Johnson

Keatley

et al. 2010

Journal of Applied Ecology

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Summary1. An important step in the conservation of biodiversity is to identify what exists, its quantity and its quality (i.e. condition). This can be a daunting task at the landscape-scale, so vegetation communities are often used as surrogates for biodiversity. Satellite imagery has improved our ability to rapidly measure vegetation parameters but the need for calibration still requires rapid and cost-effective on-ground condition assessment. Some management agencies address this need by using visual condition assessments, with unknown consequences for the different purposes of condition data. It is therefore vital to examine the comparability of visual and systematic condition assessment methods to guide their use in conservation decision making. 2. We compared visual assessments of vegetation condition with more systematic and higher resolution on-ground assessments, using a method where both assessments were made for the same quadrats. We determined both the condition parameters observers respond to when making visual assessments of condition, and the consequences of any differences for the application of these data. 3. We found that visual assessment of vegetation condition broadly represented measured assessments of the same vegetation, but that observers simplify their assessments by responding to only some of the measured condition parameters. No consistent trends were found in the parameters observers responded to across the different vegetation types sampled. 4. Synthesis and applications. We conclude that visual estimates of vegetation condition are only of sufficient resolution to replace more expensive, high-resolution assessments at a landscape-scale, when condition results are combined over multiple areas and vegetation types. Visual assessment methods potentially can provide an efficient measure of overall condition for conservation management agencies where practitioners can make assessments of condition in the course of their daily management activities -an important step forward. At smaller scales, idiosyncratic effects render visual estimates highly variable when compared with systematic condition assessments. This variability, especially among vegetation types, suggests that more systematic assessments are necessary when management decisions require higher-resolution estimates of changes in individual condition parameters, such as when measuring the success of individual management actions. These findings provide a valuable guide for selecting the most appropriate approach for the different objectives of condition assessments for biodiversity conservation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is what you see what you get? Visual vs. measured assessments of vegetation condition

Cook

Wardell‐Johnson

Keatley

et al. 2010

Journal of Applied Ecology

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“…Early research of EHA includes using keystone species (lichen or some animals) as indicators of ecosystem health [ 14 , 31 , 32 ], emphasizing specific aspects of ecosystem health (e.g., diversity–abundance relationship [ 33 ], resilience [ 34 ] or energy flow [ 35 ]), and ranking current ecosystem health by comparing current ecological status with the reference status [ 36 , 37 ]. EHA has been developed as a quantitative assessment and has been widely used since Costanza et al [ 38 ] developed an overall ecosystem health index, HI = V × O × R., where “Vigor (V) means ecosystem primary production; organization (O) means species diversity and numbers of interactions between system components; resilience (R) means system capacity to maintain structure and function in the presence of stress” [ 9 , 39 ].…”

Section: Current Methodology and Challenges For Grassland Health Assementioning

confidence: 99%

Some Insights on Grassland Health Assessment Based on Remote Sensing

Guo

2015

Sensors

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Grassland ecosystem is one of the largest ecosystems, which naturally occurs on all continents excluding Antarctica and provides both ecological and economic functions. The deterioration of natural grassland has been attracting many grassland researchers to monitor the grassland condition and dynamics for decades. Remote sensing techniques, which are advanced in dealing with the scale constraints of ecological research and provide temporal information, become a powerful approach of grassland ecosystem monitoring. So far, grassland health monitoring studies have mostly focused on different areas, for example, productivity evaluation, classification, vegetation dynamics, livestock carrying capacity, grazing intensity, natural disaster detecting, fire, climate change, coverage assessment and soil erosion. However, the grassland ecosystem is a complex system which is formed by soil, vegetation, wildlife and atmosphere. Thus, it is time to consider the grassland ecosystem as an entity synthetically and establish an integrated grassland health monitoring system to combine different aspects of the complex grassland ecosystem. In this review, current grassland health monitoring methods, including rangeland health assessment, ecosystem health assessment and grassland monitoring by remote sensing from different aspects, are discussed along with the future directions of grassland health assessment.

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“…)), which commonly have higher forage production values than native reference communities. For a more complete discussion concerning the use of ecological classifications in broad-level assessments of rangeland health and condition, the reader is referred to Jensen et al 2000.…”

Section: Management Applicationsmentioning

confidence: 99%

Spatial modeling of rangeland potential vegetation environments

Jensen¹,

Dibenedetto²,

Barber³

2001

REM

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Potential vegetation environments (e.g., habitat types, range sites, ecological sites) are important to land managers because they provide a conceptual basis for the description of resource potentials and ecological integrity. Efficient use of potential vegetation classifications in regional or subregional scale assessments of ecosystem health has been limited to date, however, because traditional ecological unit mapping procedures often treat such classifications as ancillary information in the map unit description. Accordingly, it is difficult, if not impossible, to describe the precise location, patch size, and spatial arrangement of potential vegetation environments from most traditional ecological unit maps. Recent advances in remote sensing, geographic information systems (GIS), terrain modeling, and climate interpolation facilitate the direct mapping of potential vegetation through a predictive process based on gradient analysis and ecological niche theory. In this paper, we describe how a predictive vegetation mapping process was used to develop a 30 m raster-based map of 4 grassland, 5 shrubland, and 6 woodland habitat types across the Little Missouri National Grasslands, North Dakota. Discriminant analysis was used in developing this potential vegetation map based on 6 primary geographic information system themes. Geoclimatic subsections and remotely sensed vegetation lifeform maps were used in predictive model stratification. Terrain indices, LANDSAT satellite imagery, and interpolated climate information were used as independent (predictor) variables in model construction. A total of 616 field plots with known habitat type membership were used as dependent variables and assessed by a jackknife discriminant analysis procedure. Accuracy values of our map ranged from 54 to 77% in grasslands, 62 to 100% in shrublands, and 70 to 100% in woodlands dependent on geoclimatic subsection setting. Techniques are also described for generalizing the 30 m pixel resolution habitat type map to appropriate ecological unit maps (e.g., landtype associations) for use in ecosystem health assessments and land use planning. ResumenLos ambientes de vegetación potencial (por ejemplo, tipos de hábitat, sitios de pastizal, sitios ecológicos) son importantes para el manejador de terrenos porque ellos proveen una base conceptual para la descripción de los recursos potenciales y la integridad ecológica. A la fecha, el uso eficiente de clasificaciones de vegetación potencial en evaluaciones de la salud del ecosistema a escala regional y subregional ha sido limitado, esto debido a que los procedimientos tradicionales de mapeo de unidades ecológi-cas a menudo tratan tales clasificaciones como información secundaria en la descripción de la unidad de mapeo. De acuerdo a esto, es difícil, si no imposible, describir la localidad precisa, el tamaño de parche y el arreglo espacial de los ambientes de vegetación potencial a partir de los mapas mas tradicionales de unidad ecológica. Avances recientes en el área de sensores remotos, ...

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Application of Ecological Classification and Predictive Vegetation Modeling to Broad-Level Assessments of Ecosystem Health

Cited by 6 publications

References 11 publications

Is what you see what you get? Visual vs. measured assessments of vegetation condition

Is what you see what you get? Visual vs. measured assessments of vegetation condition

Some Insights on Grassland Health Assessment Based on Remote Sensing

Spatial modeling of rangeland potential vegetation environments

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