System for automated environmental monitoring using remote sensing data of the Earth from open data sources

Environmental monitoring using remote sensing data requires an analyst to perform a large amount of routine work related to downloading, processing and analyzing data, especially in cases when the study area is covered with a large number of satellite imagery. The paper presents the results of the design and software implementation of the system that automates downloading and processing of remotely sensed data according to developed scenarios and, thus, greatly simplifies the processing of satellite imagery. It provides the description of tools for accessing data from the archive of the United States Geological Survey (USGS) and describes the data flow in the system. The paper gives an analysis of results obtained using the developed system on the example of monitoring the state of Siberian pine forests of the Tomsk region

Using Long Time Series of Satellite Remote Sensing Data to Assess the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshes, Iraq

In the recent past, the Mesopotamia region has been rich in all forms of biological diversity, characterized by a fertile living environment and natural habitats full of rare birds, wild animals, aquatic animals, and diverse plants. Its natural abundance and geographical location have allowed it to be break or transit point for millions of migratory birds from Russia to South Africa. It is a breeding ground for many species of Persian Gulf fish. Despite all this historical, environmental and economic richness, they have been neglected as a result of the combination of a number of human and climatic factors, which in 16 years (1988-2003) has modified them to a land where vegetation, water, and biodiversity have been clearly reduced. This is a great environmental loss, not only for West Asia but for the whole world. This dissertation explores the changes in the vegetation coverage and water bodies in the Mesopotamian marshes, Iraq over more than three decades (36 years) using different sources of satellite remote sensing datasets. Firstly, we utilized Normalized Difference Vegetation Index (NDVI) from the Land Long Term Data Record (LTDR) Version 5 which has a 0.05o x 0.05o in spatial resolution and daily temporal repeat to monitor the fluctuations of vegetation together with hydrological variables such precipitation, surface temperature, and evapotranspiration. In this research, we studied the impact of climate change and anthropogenic activities on vegetation and water coverage changes. Secondly, we compared Normalized Difference Vegetation Index from various satellite sensors - Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for 17 years. We selected this time series (2002-2018) to monitor the changes in vegetation area. The time series (2002-2018) is considered as a period of rehabilitation for the Mesopotamian marshes. Thirdly, as a result of human factors and local and regional climate changes, the marshes and Iraq are in general vulnerable to face a large number of dust storms annually. According to local sources (Iraq news) and National Aeronautics and Space Administration, the time period from June 29 to July 8, 2009, is considered the longest dust storm period in Iraq during last decade. In this research, we utilized the Moderate Resolution Imagining Spectroradiometer, surface reflectance daily data to calculate the Normalized Difference Dust Index. Additionally, brightness temperature data from Aqua thermal band 31 were used to separate sand on the ground from atmospheric dust. The main reasons for the degradation of the Mesopotamian marshes were due to anthropogenic activities. In the comparison research, we found that the NDVI derived from MODIS, AVHRR and Landsat sensors are correlated with high precision. This paper investigates the utility of combining low spatial resolution with frequent temporal repeat and long-term coverage and a high spatial resolution with infrequent temporal repeat and similar long-term coverage. This study also proves that we can use the low-resolution Advance Very High- resolution Radiometer data for studies on land cover change

Ekstraksi Perubahan Tutupan Vegetasi Di Kabupaten Gorontalo Menggunakan Google Earth Engine

Monitoring changes in vegetation cover is important for the restoration of ecosystems in the Gorontalo Regency area. The utilization of remote sensing technology makes it possible to detect the dynamics of changes in vegetation cover spatially and temporally. The Terra MODIS satellite image collection in the study area is available in large numbers and sizes. Therefore, cloud computing-based spatial technology support is needed. Google Earth Engine (GEE) as a geospatial computing device is an alternative to cover this shortfall. The aim of this study is to explore the condition of vegetation cover spatially and temporally using the GEE platform. A total of 43 MODIS images in the study area, recording periods 2000 and 2020, were used to quickly and effectively generate vegetation cover maps. The process of downloading, processing, and analyzing data was automated through the GEE interface. The results of the mapping in 2000 and 2020 are shown by maps of vegetation cover in two classes, namely; vegetation and non-vegetation. The accuracy of the vegetation cover map shows good results, namely an overall accuracy of 0.81 for 2000 and 0.85 for 2020. The area of the non-vegetation class increased by 2815.29 ha, and the vegetation class decreased by 2767.31 ha. The map of spatial changes in vegetation cover in the study area is classified into three classes, namely revegetation, devegetation, and unchanged. Based on these results, the extraction of vegetation cover changes in the study area using the GEE platform can be carried out well

Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018

The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of the commercial fisheries in the region. The normalized difference vegetation index (NDVI) has been derived using observations from various satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for the 17-year period between 2002 and 2018. We have chosen this time series (2002−2018) to monitor the change in vegetation of the study area since it is considered as a period of rehabilitation for the marshes (following a period when there was little to no water flowing into the marshes). Statistical analyses were performed to monitor the variability of the maximum biomass time (month of June). The results illustrated a strong positive correlation between the NDVI derived from Landsat, MODIS, and AVHRR. The statistical correlations were 0.79, 0.77, and 0.96 between Landsat and AVHRR, MODIS and AVHRR, and Landsat and MODIS, respectively. The linear slope of NDVI (Landsat, MODIS, and AVHRR) for each pixel over the period 2002−2018 displays a long-term trend of green biomass (NDVI) change in the study area, and the slope is slightly negative over most of the area. Slope values (−0.002 to −0.05) denote a slight decrease in the observed vegetation index over 17 years. The green biomass of the marshlands increased by 33.2% of the total area over 17 years. The areas of negative and positive slopes correspond to the same areas in slope map when calculated from Landsat, MODIS, and AVHRR, although they are different in spatial resolution (30 m, 1 km, and 5 km, respectively). The time series of the average NDVI (2002−2018) for three different sensors shows the highest and lowest NDVI values during the same years (for the month of June each year). The highest values were 0.19, 0.22, and 0.22 for Landsat, MODIS, and AVHRR, respectively, in 2006, and the lowest values were 0.09, 0.14, and 0.09 for Landsat, MODIS, and AVHRR, respectively, in 2003