Widespread placement and high data sampling rate of current generation of phase measurement units (PMUs) in wide area monitoring systems result in huge amount of data to be analysed and stored, making efficient storage of such data a priority. This paper presents a generalized compression technique that utilizes the inherent correlation within PMU data by exploiting both spatial and temporal redundancies. A two stage compression algorithm is proposed using principal component analysis in the first stage and discrete cosine transform in the second. Since compression parameters need to be adjusted to compress critical disturbance information with high fidelity, an automated but simple statistical change detection technique is proposed to identify disturbance data. Extensive verifications are performed using field data, as well as simulated data to establish generality and superior performance of the method. In our project we consider an ecosystem, Ecology plays an important role in agriculture crop rotation, weed control, management of grasslands, range management forestry, biological surveys, pest control, fishery biology, and in the conservation of soil, wildlife, forest, water supplies, water bodies like rivers, lakes and ponds, Collecting related features of living things in the ecosystem and creates a database. And applying the data extraction based on the conditions. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data.