Temporal variations in phenological events of forests, grasslands and desert steppe ecosystems in Mongolia: a remote sensing approach

Abstract

. Temporal variations in phenological events of forests, grasslands and desert steppe ecosystems in Mongolia: a remote sensing approach. Ann. For. Res. 59(2): 175-190.Abstract. The occurrences of phenological events are important variables in the evaluation of the influence of climate change on terrestrial ecosystems. Changes in climate can cause significant changes in the timing and duration of phenological events. Information related to large-scale phenology is therefore useful for exploring the seasonal and inter-annual variability in vegetation-climate interactions. This study aimed to obtain the timing and temporal pattern of the onset of green-up and dormancy (OG and OD) and length of growing season (LGS) using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for Forest, Grassland, and Desert steppe in Mongolia over the 10-year period from 2000 to 2009. Results demonstrated that phenological events can be differentiated by multi-temporal NDVI and EVI data and that the timing ascribed to OG and OD is slightly different between the two indices. In general, NDVI and EVI agreed that the OG of forest varied from late May to middle July. The OG of grassland and desert steppe suggested by NDVI were from late May to middle July and from middle May to middle July respectively, however EVI suggested an earlier timing of OG. NDVI and EVI also showed similar variation for the timing of OD from early September to early October. The derived LGS showed the least variation for forest, highest variation for desert steppe, and only moderate variation for grassland. Grassland and desert steppe experienced high positive and negative variations in the OG and LGS during the years from 2000 to 2009. These regions might be vulnerable to global change and are likely to be strongly affected by meteorological changes. Keywords phenological events; onset of green-up; onset of dormancy; length of growing season; peak time of vegetation maturity; vegetation indices; MODIS NDVI and EVI

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

“…Therefore, NGL can be regarded as the primary key to the response of trees to temperature change and providing key information for early detection of climate change [4]. In forestry, telemetry has been used to investigate species classification [5,6], tree delimitation [7,8], and biomass productivity evaluation [9,10]. However, NGL may have a low probability of occurrence in detection, or they may be smaller than the size of the background.…”