Humans trying to understand the current state or predict the future condition of social-ecological landscapes (SELs), as for instance driven by climate change, regularly resort to simple, easily interpreted surrogates as parts of the whole complexity that can be understood and used by non-scientists to make planning and management decisions. Yet, the overall information we can gain from a set of indicators will never match that of the whole system, since each individual indicator carries only partial information. Thus, the set of indicators needs to be constantly re-evaluated and re-interpreted in the light of the increasing understanding of the whole organization and functioning of systems. Fortunately, the complexity of living systems of people and nature emerges not from a random association of a large number of interacting factors but rather from a smaller number of key-controlling processes (Holling 2001; Gunderson and Holling 2002). Much of the fundamental nature of systems can often be captured and described by single key variables, as many features of the system’s state tend to shift in concert with a few important key-state variables (Holling 2001). Remote sensing is a primary source of information to study the complexity of SELs in terms of dynamics at multiple spatial and temporal scales. It has become a proven tool for scientists to monitor synoptically and globally, and to understand major disturbance events and their historical regimes at regional and global scales (Kerr and Ostrovsky 2003; Potter et al. 2003; Zurlini et al. 2006). It has provided valuable indices to describe and quantify natural and human-related land-cover transformations and processes and ecosystem service provisioning. One, in particular, has been widely used: the Normalized Difference Vegetation Index (NDVI). For a comprehensive review of NDVI applications, see Kerr and Ostrovsky (2003) and Pettorelli et al. (2005). Briefly, NDVI can be used to quantify annual net primary productivity (Young and Harris 2005), which is a main supporting ecosystem service (MEA 2005; Costanza et al. 2007) and it responds to temperature and water availability. NDVI is broadly recognized as a spatially explicit robust indicator of vegetation photosynthesis related to social–ecological processes such as habitat-land use conversion (e.g., urban sprawling) or crop rotation (Guerschman et al. 2003; Potter et al. 2003; Young and Harris 2005). It is used to identify and assess the impact of disturbances such as drought, fire, flood, frost (Potter et al. 2003; Mildrexler et al. 2007), or other human-driven disturbances (Guerschman et al. 2003; Zurlini et al. 2012; Wylie et al. 2008; Zaccarelli et al. 2008). Thus, NDVI related indices are, I believe, the most appropriate indicator to gauge a wide array of cross-scale impacts of climate change. However, remote sensing implies some errors in defining boundaries. The relevance of this will depend on scale range of your analyses. But given that you are looking for quality this would not be a big problem.

Humans trying to understand the current state or predict the future condition of social-ecological landscapes (SELs), as for instance driven by climate change, regularly resort to simple, easily interpreted surrogates as parts of the whole complexity that can be understood and used by non-scientists to make planning and management decisions. Yet, the overall information we can gain from a set of indicators will never match that of the whole system, since each individual indicator carries only partial information. Thus, the set of indicators needs to be constantly re-evaluated and re-interpreted in the light of the increasing understanding of the whole organization and functioning of systems. Fortunately, the complexity of living systems of people and nature emerges not from a random association of a large number of interacting factors but rather from a smaller number of key-controlling processes (Holling 2001; Gunderson and Holling 2002). Much of the fundamental nature of systems can often be captured and described by single key variables, as many features of the system’s state tend to shift in concert with a few important key-state variables (Holling 2001). Remote sensing is a primary source of information to study the complexity of SELs in terms of dynamics at multiple spatial and temporal scales. It has become a proven tool for scientists to monitor synoptically and globally, and to understand major disturbance events and their historical regimes at regional and global scales (Kerr and Ostrovsky 2003; Potter et al. 2003; Zurlini et al. 2006). It has provided valuable indices to describe and quantify natural and human-related land-cover transformations and processes and ecosystem service provisioning. One, in particular, has been widely used: the Normalized Difference Vegetation Index (NDVI). For a comprehensive review of NDVI applications, see Kerr and Ostrovsky (2003) and Pettorelli et al. (2005). Briefly, NDVI can be used to quantify annual net primary productivity (Young and Harris 2005), which is a main supporting ecosystem service (MEA 2005; Costanza et al. 2007) and it responds to temperature and water availability. NDVI is broadly recognized as a spatially explicit robust indicator of vegetation photosynthesis related to social–ecological processes such as habitat-land use conversion (e.g., urban sprawling) or crop rotation (Guerschman et al. 2003; Potter et al. 2003; Young and Harris 2005). It is used to identify and assess the impact of disturbances such as drought, fire, flood, frost (Potter et al. 2003; Mildrexler et al. 2007), or other human-driven disturbances (Guerschman et al. 2003; Zurlini et al. 2012; Wylie et al. 2008; Zaccarelli et al. 2008). Thus, NDVI related indices are, I believe, the most appropriate indicator to gauge a wide array of cross-scale impacts of climate change. However, remote sensing implies some errors in defining boundaries. The relevance of this will depend on scale range of your analyses. But given that you are looking for quality this would not be a big problem.

”Land use maps are generally too imprecise to obtain useful information about habitat structure in agricultural landscapes”

Well, if land use maps would be proper for informing about habitat structure... then probably they would have been named ”habitat structure maps”, not ”land use maps”.

Yes, land use maps - as those produced in CLC (Corine Land Cover) program - are not the ones to help You in an attempt to establish habitat structures in various polygons in those maps (digital ones or else...).

We have done a lot of habitat/vegetation condition modelling in NSW, Australia. We have adopted a framework that recognises multiple condition components e.g. canopy, mid-storey, ground-cover. We rely on remote sensing for the woody components (canopy, mid-storey) which is readily detected; and fall back to using landuse and other surrogates for native ground-cover, and sometimes things like invasive weeds, coarse woody debris, because these cannot reliably be measured remotely.

I have included a sample from a paper in review. There is a 2006 special issue of Ecological Management and Restoration Vol.7 S1 on various aspects of vegetaton condition modelling. I have a paper there on modelling veg condition dynamics.

The rangeland ecology scientists have been straggling with that question for more than half a century, ever since Dyksterhuis ‘s 1949 landmark paper. They have recently arrived at the concept of rangeland ecosystem health which involves many elements including soils, plants , animals and man from a structural/ functional view point. I feel agricultural researchers could benefit from examining that approach and from consider its adaptation adaptation to agriculture land quality. See for example: http://jornada.nmsu.edu/sites/jornada.nmsu.edu/files/Briske_REMsynth_2005.pdf

I have an idea that one would have to sample the different agricultural landscapes on to observe gradient, diversity and abundance of the species. I believe different ag, areas have different levels of disturbance and species due to varying environs might respond differently to such pressures. If you use sat images then do on ground plots. Please read some works by Ed Witkoski from the University of Witwatersrand South Africa, you can send mail to him as well.

Satellite images with fine resolution (e.g., ASTER, ETM+, or Quickbird) and aerial photographs may work for the evaluation on habitat quality in agricultural landscape.

These have been fine answers, and I would support the diversity of assessment approaches recommended in these responses. My personal bias is that a tremendous amount of information can be gleaned from indices of biotic integrity. With respect to grassland obligate birds, their occurrence, abundance, community composition, and measures of fitness convey and integrate information from broad to fine scales. An agricultural landscape that supports excellent representation of its native grassland obligates is likely to be highly connected (as opposed to fragmented), to represent heterogenous land cover that provides opportunities for species that rely on closely cropped or tall and dense vegetation, and to provide the specific microhabitat needs (e.g., food, thermal cover) of its component species.

The Bird Community Index can do this, but it tends to have greater utility in more forested landscapes. There are likely some European indices that address grassland birds explicitly; I'm working on a new model for the U.S. Southern Plains, but it's still at least a year or two from full development. You might check Coppedge, B. R., D. M. Engle, R. E. Masters, and M. S. Gregory. 2001. Avian response to landscape change in fragmented southern Great Plains grasslands. Ecological Applications 11:47–59.