Thorsten Dahms

Seasonal-based analysis of vegetation response to environmental variables in the mountainous forests of Western Himalaya using Landsat 8 data

ABSTRACT The health (or greenness) of the mountainous vegetation varies with seasons depending on its type and local topographic and climatic conditions. The forests in the Western Himalayas are influenced by variables such as precipitation and temperatures through seasons with considerable inter-annual variability. This study presents the phenological behaviour of the moist deciduous forests (MDFs) in Doon Valley of Uttarakhand, India, during 2013–2015 using medium spatial resolution data set. We proposed a new index called the temporal normalized phenology index (TNPI) to quantify the change in trajectories of Landsat 8 OLI-derived normalized difference vegetation index (NDVI) during two time steps of the vegetation growth cycle. To establish the associations amongst a set of environmental factors and vegetation greenness during different seasons, multiple regression analysis was carried out with sample-based TNPI values as response variable and elevation, slope, aspect, and Landsat 8-derived land surface temperature (LST) as explanatory variables. Our results indicated that major changes in NDVI values occur between April (transitional month of leafing phenophase and starting of leaf flush activity) and September (end of leaf flush activity). Furthermore, interactions amongst environmental variables (elevation, LST, and precipitation) are strongly correlated with changes in vegetation greenness between April and September, whereas they show lesser correlations as stand-alone factors. The pronounced effect of the change in LST (LST) was observed in lower elevation areas (400–600 m), which resulted in the change in vegetation greenness between leaf fall and leaf flush activity. In conclusion, cross-validated statistics confirmed that TNPI may be used as a better alternative for the analysis of temporal phenology cycle between two time steps of maximum and minimum vegetation growth periods. This may reduce the requirement of large time-series remote-sensing data sets for long-term vegetation phenology analysis.

Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles

Tree mortality caused by outbreaks of the bark beetle Ips typographus (L.) plays an important role in the natural dynamics of Norway spruce (Picea abies L.) stands, which could cause far-reaching changes in the occurrence and duration of vegetation phenology. Field-based early detection of tree disturbances is hampered by logistic, terrain, and technical shortcomings, and by the inability to continuously monitor disturbances over large areas. Despite achievements in remote mapping of bark-beetle-induced tree mortalities, early warning has been mostly unsuccessful mainly because of the lack of spectral sensitivity and discrepancies in definitions of field- and image-based disturbance classes. Here we applied a method based on inter-annual phenology of Norway spruce stands derived from synthetic multispectral data to part of the Bavarian Forest National Park in Germany. We fused temporally continuous Moderate Resolution Imaging Spectroradiometer and discrete RapidEye data using a flexible spatiotemporal data fusion method to achieve validated 8-day RapidEye-like composites of normalized difference vegetation index for 2011. We assumed that the dead trees delineated on 2012 aerial photographs were those in which bark beetle infestations were initiated in 2011. Samples were drawn with variable-sized buffering to represent the areas prone to infestations and their surroundings. We applied a conditional inference random forest to select the best image date among the entire 46 synthetic datasets to best discriminate between the core infestation patches and their surroundings from the subsequent year. Of the discrete time points identified, day 281 of the year represented the highest discrepancy between aerial image-based dead trees and their surroundings. Classification results were significantly correlated with beetle count data obtained using pheromone traps. Our method provided valuable information for management purposes and enabled wall-to-wall mapping of stands prone to infestation and its uncertainty. The results offer potential implications for rapid and cost-effective monitoring of bark beetle outbreaks using satellite data, which would be of great benefit for both management and research tasks.

Phenological NDVI time series for the dynamic derivation of soil coverage information

Monitoring of agricultural used soils at frequent intervals is needed to get a better understanding of processes like soil erosion or harvest forecast. This is crucial to support decision making and refining soil policies especially in the context of climate change. Parcel-specific soil coverage information can be derived by satellite imagery with high temporal and geometric resolution. However, their usable number is mostly, due to cloud cover, not representative for the phenological characteristics of vegetated classes. To overcome temporal constraints, spatial and temporal fusion models like STARFM or ESTARFM are increasingly applied to derive high resolution time series of remotely sensed biophysical parameters based on high-spatial/low-temporal resolution imagery like Landsat or Sentinel-2 and low-spatial/high-temporal resolution imagery like MODIS. We show how their combination with corresponding phenological information enables the definition of temporal windows in which models predicting fractional vegetation coverage (FV C) or bare soils (BS) can be selectively applied.

Semi-Autonomous Remote Sensing Time Series Generation Tool

High spatial and temporal resolution data is vital for crop monitoring and phenology change detection. Due to the lack of satellite architecture and frequent cloud cover issues, availability of daily high spatial data is still far from reality. Remote sensing time series generation of high spatial and temporal data by data fusion seems to be a practical alternative. However, it is not an easy process, since it involves multiple steps and also requires multiple tools. In this paper, a framework of Geo Information System (GIS) based tool is presented for semi-autonomous time series generation. This tool will eliminate the difficulties by automating all the steps and enable the users to generate synthetic time series data with ease. Firstly, all the steps required for the time series generation process are identified and grouped into blocks based on their functionalities. Later two main frameworks are created, one to perform all the pre-processing steps on various satellite data and the other one to perform data fusion to generate time series. The two frameworks can be used individually to perform specific tasks or they could be combined to perform both the processes in one go. This tool can handle most of the known geo data formats currently available which makes it a generic tool for time series generation of various remote sensing satellite data. This tool is developed as a common platform with good interface which provides lot of functionalities to enable further development of more remote sensing applications. A detailed description on the capabilities and the advantages of the frameworks are given in this paper.