The growth patterns of emergent vegetation can be a useful indicator for factors affecting lake health. However, field data to characterize emergent vegetation at many reservoirs may not be available or may be limited to small, isolated areas. We present a case study using remotely sensed data from the Landsat satellite to generate data to represent emergent vegetation in the near-shoreline and tributary delta areas of Malheur Lake, Oregon. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area on the day of the satellite collection, average daily maximum temperatures for April, May, June, and July, and average daily precipitation in June, all parameters that could affect vegetation. To estimate historic emergent vegetation extent, we computed the Normalized Difference Vegetation Index (NDVI) for 30 years of Landsat satellite images from 1984 to 2013. Around Malheur Lake we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas). For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels with NDVI values greater than 0.2. We measured lake area by computing the Modified Normalized Difference Water Index (MNDWI) and computing the area by summing the pixels that indicated water. We compared NDVI time-series values with the time series for lake area, June precipitation, and maximum daily temperatures for April, May, June, and July to determine if these parameters were correlated. Correlation would imply that emergent vegetation was influenced by the parameter. We found that correlations of vegetative extent in any of the eight ROIs with the selected parameters were minimal, indicating that there are other factors besides the ones chosen that drive emergent vegetation levels in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail for quantification and description of ecosystem changes and thus offer a good source of information to understand historic trends in reservoir health. We expect that future work will explore other potential drivers for emergent vegetation extent, such as carp populations in Malheur Lake which are known to affect emergent vegetation. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.

Nowadays remote sensing is a well-established method and technique of providing data. The current development shows the availability of systems with very high geometric resolution for the monitoring of vegetation. At the same time, however, the value of temporally high-resolution data is underestimated, particularly in applications focusing on the detection of short-term changes. These can be natural processes like natural disasters as well as changes caused by anthropogenic interventions. These include economic activities such as forestry, agriculture or mining but also processes which are intended to convert previously used areas into natural or near-natural surfaces. The Königsbrücker Heide is a former military training site located about 30 km north of the Saxon state capitol Dresden. After the withdrawal of the Soviet forces in 1992 and after nearly 100 years of military use this site was declared as nature reserve in 1996. The management of the whole protection area is implemented in three different management zone. Based on MODIS-NDVI time series between 2000 and 2016 different developments are apparent in the nature development zone and the zone of controlled succession. Nevertheless, the analyses also show that short-term changes, so called breaks in the vegetation development cannot be described using linear trend models. The complete understanding of vegetation trends is only given if discontinuities in vegetation development are considered. Structural breaks in the NDVI time series can be found simultaneously in the whole study area. Hence it can be assumed that these breaks have a more natural character, caused for example by climatic conditions like temperature or precipitation. Otherwise, especially in the zone of controlled succession structural breaks can be detected which cannot be traced back to natural conditions. Final analyses of the spatial distribution of breakpoints as well as their frequency depending on the respective protection zone allow a detailed view to vegetation development in the Köonigsbrüucker Heide.

In 1989 about 1.5 million soldiers were stationed in Germany. With the political changes in the early 1990s a substantial decline of the staff occurred on currently 200,000 employees in the armed forces and less than 60,000 soldiers of foreign forces. These processes entailed conversions of large areas not longer used for military purposes, especially in the new federal states in the eastern part of Germany. One of these conversion areas is the former military training area Königsbrück in Saxony. For the analysis of vegetation and its development over time, the Normalized Difference Vegetation Index (NDVI) has established as one of the most important indicators. In this context, the questions arise whether MODIS NDVI products are suitable to determine conversion processes on former military territories like military training areas and what development processes occurred in the 'Königsbrücker Heide' in the past 15 years. First, a decomposition of each series in its trend component, seasonality and the remaining residuals is performed. For the trend component different regression models are tested. Statistical analysis of these trends can reveal different developments, for example in nature development zones (without human impact) and zones of controlled succession. The presented work ow is intended to show the opportunity to support a high temporal resolution monitoring of conversion areas such as former military training areas.

The value of remote sensing data is particularly evident where an areal monitoring is needed to provide information on the earth's surface development. The use of temporal high resolution time series data allows for detecting short-term changes. In Kogi State in Nigeria different vegetation types can be found. As the major population in this region is living in rural communities with crop farming the existing vegetation is slowly being altered. The expansion of agricultural land causes loss of natural vegetation, especially in the regions close to the rivers which are suitable for crop production. With regard to these facts, two questions can be dealt with covering different aspects of the development of vegetation in the Kogi state, the determination and evaluation of the general development of the vegetation in the study area (trend estimation) and analyses on a short-term behavior of vegetation conditions, which can provide information about seasonal effects in vegetation development. For this purpose, the GIMMS-NDVI data set, provided by the NOAA, provides information on the normalized difference vegetation index (NDVI) in a geometric resolution of approx. 8 km. The temporal resolution of 15 days allows the already described analyses. For the presented analysis data for the period 1981-2012 (31 years) were used. The implemented work flow mainly applies methods of time series analysis. The results show that in addition to the classical seasonal development, artefacts of different vegetation periods (several NDVI maxima) can be found in the data. The trend component of the time series shows a consistently positive development in the entire study area considering the full investigation period of 31 years. However, the results also show that this development has not been continuous and a simple linear modeling of the NDVI increase is only possible to a limited extent. For this reason, the trend modeling was extended by procedures for detecting structural breaks in the time series.