**4. Overall conclusions**

Multi-temporal remote sensing offers countless opportunities for monitoring past and present changes in land cover and land use. By monitoring the size and shape of water bodies, we can infer on human pressure and climate change. Small water bodies are especially fragile areas with a very high ecological value (the value of the services provided by lakes and wetlands has been considered as high as 8.498 and 14.785 \$*ha*−1*yr*−<sup>1</sup> respectively according to Costanza et al., 1997) that are very sensitive to changes in temperature or the equilibrium of nutrients input (Mitsch & Gosselink, 2000).

In this chapter, two new approaches for monitoring small lakes and wetlands were used. First by using a region-based unsupervised classification based on an hybrid implementation (watershed and Markov random fields) we ensured a non-arbitrary systematic approach that did not rely on training samples or a subjective threshold. The MAGIC program proved very reliable for processing a large number of scenes while maintaining a very stable and predictable behavior. Although it turned out to work better by choosing a larger number of classes than actually needed, finding the water class was always easy and could easily be automated in certain cases like this one (for example by ordering the signatures through their mean). Future work will concentrate on determining the parameters that govern the precise amount of water within a pixel for it to fall in the water class.

Secondly, an interpolation method was used to artificially increase the resolution (from 30 m to 5 m) of a series of Landsat images to improve the contour definition of a set of very small lakes and to characterize their dynamic throughout a 25 years period. Much care was taken to validate the methodology by using two distinct methods of validation to account for all type of errors. The validation yielded a precision between 80% and 93% in all cases except one. Future work will concentrate on having this approach improve by using precise elevation data to associate an actual water level with the size of the lakes.

The use of historical satellite data is often made difficult by the absence of validation data and one must generally rely of sparse observations to corroborate results. One solution lies on validating the methodology using recent data and then to apply it to the historical data. Landsat has been an invaluable source of data since the 80's (Thematic Mapper) and even the 70's (Multi Spectral Scanner) by systematically acquiring data at regular predictable intervals over the same region. The newer generations of satellites platforms work mostly on a "per demand" scheme and require more carefully planned logistics of image acquisition. It is also likely that future post-Landsat multi-temporal studies will have to deal with data from different sensors with different resolutions and even different spectral specifications. This will bring new challenges to multi-temporal studies for which much research is still needed.

#### **5. Acknowledgements**

The authors are thankful to the Forestry Institute of Minas Gerais (IEF-MG) for providing the Ikonos and RapidEye data and field support. We are most thankful to Thaís Amaral Moreira for her hard work in mapping and statistics.

#### **6. References**

22 REMOTE SENSING

elevation surface produced from ASTER data with a resolution of 30 m, made it possible to estimate that the aquifer lowered, during the 25 year period (1984-2009), by up to two meters. The water balance using the Thornthwaite approach is well suited for area with limited climatological information and provides valuable insight on the climatological condition ruling water availability. In the present case, the water balance could not be statistically correlated (Spearman's correlation) to the shrinking of six small lakes in Northern Minas Gerais, Brazil. It became clear that, if the present situation continues, these small lakes (and the nearby palm swamps) will disappear with drastic consequences for the populations of

Future studies will concentrate on matching the lake size with precise elevation data and piezometric measurements. Although Landsat data proved most useful for extracting the open water surface, we plan to shift towards more precise satellite data such as RapidEye for which the Minas Gerais Government is acquiring on a regular base (twice a year) for the whole state. Future research will also explore more thoroughly the possibilities of artificially increasing resolution through interpolation. More interpolations methods need to be tested and compared with various situations. With the recent installation of a nearby weather station, precise local data will yield better control on monitoring the water budget throughout the year.

Multi-temporal remote sensing offers countless opportunities for monitoring past and present changes in land cover and land use. By monitoring the size and shape of water bodies, we can infer on human pressure and climate change. Small water bodies are especially fragile areas with a very high ecological value (the value of the services provided by lakes and wetlands has been considered as high as 8.498 and 14.785 \$*ha*−1*yr*−<sup>1</sup> respectively according to Costanza et al., 1997) that are very sensitive to changes in temperature or the equilibrium of nutrients

In this chapter, two new approaches for monitoring small lakes and wetlands were used. First by using a region-based unsupervised classification based on an hybrid implementation (watershed and Markov random fields) we ensured a non-arbitrary systematic approach that did not rely on training samples or a subjective threshold. The MAGIC program proved very reliable for processing a large number of scenes while maintaining a very stable and predictable behavior. Although it turned out to work better by choosing a larger number of classes than actually needed, finding the water class was always easy and could easily be automated in certain cases like this one (for example by ordering the signatures through their mean). Future work will concentrate on determining the parameters that govern the precise

Secondly, an interpolation method was used to artificially increase the resolution (from 30 m to 5 m) of a series of Landsat images to improve the contour definition of a set of very small lakes and to characterize their dynamic throughout a 25 years period. Much care was taken to validate the methodology by using two distinct methods of validation to account for all type of errors. The validation yielded a precision between 80% and 93% in all cases except one. Future work will concentrate on having this approach improve by using precise elevation

The use of historical satellite data is often made difficult by the absence of validation data and one must generally rely of sparse observations to corroborate results. One solution lies

humans and animals.

**4. Overall conclusions**

input (Mitsch & Gosselink, 2000).

amount of water within a pixel for it to fall in the water class.

data to associate an actual water level with the size of the lakes.


**3** 

*1China 2USA 3Finland* 

**Satellite-Based Snow** 

*Chinese Academy of Sciences, Beijing* 

*University of California, Santa Barbara 3Finnish Meteorological Institute (FMI), Arctic Research Centre, Sodänkylä* 

*1Center for Earth Observation and Digital Earth,* 

*2Institute for Computational Earth System Science,* 

**Cover Analysis and the Snow Water** 

, Huadong Guo1, Jiancheng Shi2 and Juha Lemmetyinen3

**Equivalent Retrieval Perspective over China** 

Global changing is a great challenge that affects the nowadays world, even arises and becomes kinds of the political issues. The changing of the snow is not only a sensitive factor act as a driving force but can be influenced much in the global temperature variation, especially for the seasonal snow cover which is vastly distributed over the northern hemisphere. Snow cover influences the atmosphere and ocean, and therefore the climate system, through both direct and indirect effects (Judah, 1991). In the climate regime, the snow cover alters the surface energy and water circle in a global scale in the climate processing (Fg.1). From the IPCC (2001), the recent and anticipated reductions in snow cover due to future greenhouse warming are an important topic for the global change community. Large seasonal variations in snow cover are of importance on continental to hemispheric scales induces to investigate its natural variability in the climate-system forcing of such trends, versus possible anthropogenic influences (Roger, 2002). So, understanding the spatial pattern in the temporal variability of snow cover increase the current understanding of global climate change and provide a mechanism for exploring future trends ( Steve Vavrus, 2007) . As such, snow cover is an appropriate indicator of climate perturbations and may be a suitable surrogate for investigations of climate change (Serreze , 2000; IPCC, 2001;

Recent research result over China area revealed that the long time series snow trend is not suit for the whole trend over northern hemisphere and regional northern American (Qin, 2006; Xu, 2007; Wang, 2008;). From Qin's research (2006) of snow cover for the period of 1951

Yubao Qiu1<sup>∗</sup>

**1. Introduction** 

 ∗

Corresponding Author

Roger, 2002; Wulder, 2007; IPCC AR4, 2007).

