**Landscape Environmental Monitoring: Sample Based Versus Complete Mapping Approaches in Aerial Photographs**

Habib Ramezani1, Johan Svensson1 and Per-Anders Esseen2 *1Department of Forest Resource Management, Swedish University of Agriculture Science, Umeå, 2Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden* 

#### **1. Introduction**

204 Environmental Monitoring

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Unknown land use premises are to be expected due to changing conditions, e.g. shifting land use priorities, climate change, globalizing natural resource markets or new products in the natural resource sector. As a result the need is obvious for accurate, relevant and applicable landscape data to be used in cause–and–effect analysis concerning changes in environmental conditions (Ståhl et al., 2011).

The current land use strongly influence landscape structure (composition and configuration) and contribute to biodiversity loss (Hanski, 2005; Fischer and Lindenmayer, 2007). In order to consider current status and also to monitor trends within a landscape there is a need for reliable and continuous information as a basis for policy– and strategic – as well as operational decision making (Bunce et al., 2008). For this purpose, many countries have now established or are in the process of establishing monitoring programs that provide information on large spatial scale (e.g., regional and national levels), for instance, the National Inventory of Landscapes in Sweden (NILS) (Ståhl et al., 2011), the Norwegian 3Q (NIJOS, 2001), and similar programs in other countries, e.g., in Hungary (Takács and Molnár, 2009). A major concern in landscape monitoring at national scale is the large complexity and amount of data, and the consequently the labor need in data acquisition, database management as well as data analysis and interpretation.

Description and assessment of landscape conditions and changes require relevant, accurate and applicable landscape metrics, which are defined based on measurable attributes of landscape elements such as patches or boundaries. The suite of metrics must cover both the composition and configuration of the landscape to have potential to detect changes within a given landscape or when comparing different landscapes.

Calculation of landscape metrics is commonly conducted on completely mapped areas based on remotely sensed data. FRAGSTATS (McGarigal and Marks, 1995) is a frequently used software for this purpose. In mapping, homogenous areas are first delineated as polygons. Aerial photo interpretation is usually performed using a manual approach while some automated and computer–assisted approaches have recently become available (e.g., Blaschke, 2004). Important attributes in manual interpretation include tone, pattern, size and

Landscape Environmental Monitoring:

**2. Material and methods** 

habitats and the land base of Sweden (see Fig. 2).

**2.1 Study area** 

Sample Based Versus Complete Mapping Approaches in Aerial Photographs 207

advantages and limitations of complete mapping versus sample based approaches for estimating landscape metrics Shannon's diversity, total edge length and contagion from aerial photos. The specific objectives are: (1) to compare point and line intersect sampling for selected metrics in terms of the level of detail and accuracy of data extracted, and the time needed (cost) to extract the data, (2) to compare sample based and complete mapping approaches in terms of time needed, and (3) to investigate statistical properties (bias and

The data was collected from aerial photographs and land cover maps from the NILS program (Ståhl et al., 2011), which covers the whole of Sweden. NILS was developed to monitor conditions and trends in land cover classes, land use and biodiversity at multiple spatial scales (point, patch, landscape) as basic input to national and international environmental frameworks and reporting schemes. NILS was launched in 2003 and has developed a monitoring infrastructure that is applicable for many different purposes. The basic outline is to combine 3-D interpretation of CIR (Color Infra Red) aerial photos with field inventory on in total of 631 permanent sample plots (5 km × 5 km) across all terrestrial

Fig. 2. Illustration of systematic distribution of 631 NILS 1 km × 1 km sample plot across Sweden with ten strata. The density of plots varies among the strata (Ståhl et al., 2011).

RMSE) of estimators of selected metrics using Monte-Carlo sampling simulation.

shape (Morgan et al., 2010). The experience of the interpreters is critical and the results from manual interpretation are thus often more accurate than those from automated approaches. However, the manual approach may be time-consuming (Corona et al., 2004), subjective (interpreter-dependent) and considerable variation may occur between photo interpreters. The automated approach is sometimes unreliable, for instance, when land cover classes that are similar in terms of spectral reflectance should be separated (Wulder et al., 2008). In addition, overall time, including delineation and corrections may be large if an inappropriate automated approach is chosen.

Sample based approach is an interesting alternative to extract landscape data compared to complete mapping (Kleinn and Traub, 2003). The argument is that a sample survey takes less time; that it is possible to achieve more accurate result in a well-designed and wellexecuted sample survey; and that data can be acquired and analyzed more efficiently (Raj, 1968; Cochran, 1977). The efficiency and speed in delivering results is of particular interest in landscape–scale monitoring programs where stakeholders commonly are closely involved and expect outputs within reasonable time. Figure 1 shows examples of complete mapping and sample based approaches (point and line intersect sampling methods) over 1 km × 1 km aerial photo from NILS.

Fig. 1. Examples of complete mapping and sample based approaches to extract landscape metrics in 1 km × 1 km aerial photo. A) Complete mapping, B) systematic point sampling with fixed buffer (40 m), C) point pairs sampling, and D) systematic line intersect sampling.

Since aerial photos are important source of data for many ongoing environmental monitoring programs such as NILS (Ståhl et al., 2011), there is an urgent need to investigate the possibilities and limitations of both mapping and sample based approaches for estimating landscape metrics. The overall objective of this chapter is to compare the advantages and limitations of complete mapping versus sample based approaches for estimating landscape metrics Shannon's diversity, total edge length and contagion from aerial photos. The specific objectives are: (1) to compare point and line intersect sampling for selected metrics in terms of the level of detail and accuracy of data extracted, and the time needed (cost) to extract the data, (2) to compare sample based and complete mapping approaches in terms of time needed, and (3) to investigate statistical properties (bias and RMSE) of estimators of selected metrics using Monte-Carlo sampling simulation.
