A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region

An investigation of the growth status of 19-year-old Idesia polycarpa 'Yuji' plantation forest in the mountainous region of Henan, China

Plantation forests play an important role in the mitigation of greenhouse gas emissions. Idesia polycarpa Maxim is an emerging woody oil tree species in most Asian countries. The 19-year-old Idesia polycarpa 'Yuji' plantation forest was selected as a sample site. The nutrient contents of the understory soil total carbon (TC), total nitrogen (TN), nitrate nitrogen (NN), organic carbon (OC), available phosphorus (AP), available potassium (AK), and pH were analyzed. Several metrics were measured to quantify the growth status of the forest, such as tree heights (H), clear bole heights (CBH), diameters at breast height (DBH), and male-to-female ratios (MFR). In addition, we harvested the fruits to analyze oil content and fatty acid composition. The results found that the nutrient content of the soil was TC (4.93%), TN (0.42%), NN (43.08 mg kg-1), OC (4.90 g kg-1), AP (13.66 mg kg-1), AK (30.48 mg kg-1), and pH (7.90). The growth characteristics were H (11.75 m), DBH (12.79 cm), and CBH (6.17 m). The MFR was close to 1:1. Besides, the oil content of the fruit and unsaturated fatty acids was 24.08% and 68.49%, respectively. As an alternative tree species, the plantation of Idesia polycarpa offers great potential in artificial afforestation in some particular places with specific forest site conditions.

The Application of Satellite Image Analysis in Oil Spill Detection

In recent years, there has been an increasing use of satellite sensors to detect and track oil spills. The satellite bands, namely visible, short, medium infrared, and microwave radar bands, are used for this purpose. The use of satellite images is extremely valuable for oil spill analysis. With satellite images, we can identify the source of leakage and assess the extent of potential damage. However, it is not yet clear how to approach a specific leakage case methodologically. The aim of this study is the remote sensing analysis of environmental changes with the development of oil spill detection processing methods. Innovative elements of the work, in addition to methodological proposals, include the long-term analysis of surface water changes. This is very important because oil is very likely to enter the soil when water levels change. The classification result was satisfactory and accurate by 85%. The study was carried out using images from Landsat 5, Landsat 7, Landsat 8, Sentinel-1, and Sentinel-2 satellites. The results of the classification of the oil stains in active and passive technologies differ. This difference affects the methodology for selecting processing methods in similar fields. In the case of this article, the oil spill that occurred on 29 May 2020 in Norilsk was investigated and compared with data from other years to determine the extent of biodegradation. Due to the tank failure that occurred at the Nornickel power plant on that day, a large amount of crude oil leaked into the environment, contaminating the waters and soil of local areas. Research shows that oil spills may be caused by human error or may be the effect of climate change, particularly global warming.