Monitoring heterogeneous urban and rural area using Normalised Difference Infrared Index – a case study from SE Poland

Maja Bryk,Beata Kołodziej

doi:10.1080/22797254.2019.1604080

Maja Bryk, Beata Kołodziej

Open Access

https://doi.org/10.1080/22797254.2019.1604080

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Abstract

ABSTRACT The results of a study of NDII applicability for monitoring of a heterogeneous rural and urban area during a growing season (April–November) were shown. We evaluated the spatial and quantitative distributions, and temporal trends of NDII derived from Landsat 8 imagery, NDII = (NIR–SWIR)/(NIR+SWIR) or NDII = (Band5–Band6)/(Band5+Band6), over a typical medium-sized European city with its surroundings. The temporal changes of NDII were also correlated with sunshine duration, temperature, and precipitation. Vegetation was the component of land cover that was changing to the highest degree during the growing season. The most dynamic temporal alterations of NDII were recorded within the arable land. In forest areas and grassland NDII was relatively stable through the summer. The NDII values were stable also in the built-up area in view of practically constant state of human-made covers in the studied period. NDII can support the monitoring of land cover/land use in the urban and rural areas, provided that the images of fully developed plant cover are used. It was stated that NDII correlated with weather conditions. The relationships between NDII and precipitation, temperature, and sunshine duration could facilitate risk assessment and planning of agronomic practices with regard to the weather.

Highlights

The landscape of Europe is characterized by numerous medium-sized cities with up to 500,000 citizens
Vegetation was the component of land cover that was changing to the highest degree during the growing season
We evaluated the spatial and quantitative distributions, and temporal trends of Normalised Difference Infrared Index” (NDII) over a typical medium-sized European city with its surroundings

Summary

Introduction

The landscape of Europe is characterized by numerous medium-sized cities with up to 500,000 citizens. A combination of the reflectance in the R, NIR, and SWIR band yields a number of indices widely used in the synoptic monitoring of vegetation intra- and inter-annual phenology in a variety of ecosystems: cropland, forests, grassland, urban greenery, etc. This subject is widely discussed and researched by many scientists. Zhumanova, Mönnig, Hergarten, Darr, and Wrage-Mönnig (2018) assessed vegetation degradation, i.e. qualitative changes in vegetation cover in semi desert, mountain steppe, subalpine meadow-steppe, and alpine ecological zones in Kyrgyzstan with NDVI They stated that vegetation degraded pastures could be detected at different phenology stages based on remote sensing data. Vegetation indices were used for identification of transition dates for vegetation activity: greenup, maturity, senescence, and dormancy, while monitoring annual cycles of vegetation phenology with EVI for the region of New England, USA, for different land cover types in the region: deciduous broad leave forests, mixed forests, croplands, and urban (Zhang et al, 2003). Hufkens et al (2012) studied phenological dynamics of broadleaf

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