Integrated Evaluation of Vegetation Drought Stress through Satellite Remote Sensing

In the coming decades, Bulgaria is expected to be affected by higher air temperatures and decreased precipitation, which will significantly increase the risk of droughts, forest ecosystem degradation and loss of ecosystem services (ES). Drought in terrestrial ecosystems is characterized by reduced water storage in soil and vegetation, affecting the function of landscapes and the ES they provide. An interdisciplinary assessment is required for an accurate evaluation of drought impact. In this study, we introduce an innovative, experimental methodology, incorporating remote sensing methods and a system approach to evaluate vegetation drought stress in complex systems (landscapes and ecosystems) which are influenced by various factors. The elevation and land cover type are key climate-forming factors which significantly impact the ecosystem’s and vegetation’s response to drought. Their influence cannot be sufficiently gauged by a traditional remote sensing-based drought index. Therefore, based on differences between the spectral reflectance of the individual natural land cover types, in a near-optimal vegetation state and divided by elevation, we assigned coefficients for normalization. The coefficients for normalization by elevation and land cover type were introduced in order to facilitate the comparison of the drought stress effect on the ecosystems throughout a heterogeneous territory. The obtained drought coefficient (DC) shows patterns of temporal, spatial, and interspecific differences on the response of vegetation to drought stress. The accuracy of the methodology is examined by field measurements of spectral reflectance, statistical analysis and validation methods using spectral reflectance profiles.

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Effect of land-cover type and season in microwave remote sensing of snow

Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1993.322645 ◽

2002 ◽

Cited By ~ 1

Author(s):

V. Jaaskelainen ◽

L. Kurvonen ◽

M. Hallikainen

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Microwave Remote Sensing ◽

Land Cover Type ◽

Cover Type

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Estimating land cover type and proportion of vegetation using remote sensing

2014 22nd Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2014.6830289 ◽

2014 ◽

Author(s):

Cernii Berin Eroi ◽

Suleyman Gokhun Tanyer

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Cover Type ◽

Cover Type

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Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

Journal of Plant Research ◽

10.1007/s10265-021-01302-7 ◽

2021 ◽

Author(s):

Hibiki M. Noda ◽

Hiroyuki Muraoka ◽

Kenlo Nishida Nasahara

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Spectral Reflectance ◽

Canopy Structure ◽

Terrestrial Ecosystems ◽

Optical Remote Sensing ◽

Canopy Reflectance ◽

Broadleaf Forest ◽

Leaf Optical Properties ◽

Deciduous Broadleaf Forest

AbstractThe need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

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Land Use and Land Cover Change Assessment in the Context of Flood Hazard in Lagos State, Nigeria

Water ◽

10.3390/w13081105 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1105

Author(s):

Dorcas Idowu ◽

Wendy Zhou

Keyword(s):

Land Use ◽

Land Cover ◽

Flood Hazard ◽

The State ◽

Land Cover Type ◽

Lulc Change ◽

Cover Type ◽

Lulc Changes ◽

Lagos State ◽

Very High

Incessant flooding is a major hazard in Lagos State, Nigeria, occurring concurrently with increased urbanization and urban expansion rate. Consequently, there is a need for an assessment of Land Use and Land Cover (LULC) changes over time in the context of flood hazard mapping to evaluate the possible causes of flood increment in the State. Four major land cover types (water, wetland, vegetation, and developed) were mapped and analyzed over 35 years in the study area. We introduced a map-matrix-based, post-classification LULC change detection method to estimate multi-year land cover changes between 1986 and 2000, 2000 and 2016, 2016 and 2020, and 1986 and 2020. Seven criteria were identified as potential causative factors responsible for the increasing flood hazards in the study area. Their weights were estimated using a combined (hybrid) Analytical Hierarchy Process (AHP) and Shannon Entropy weighting method. The resulting flood hazard categories were very high, high, moderate, low, and very low hazard levels. Analysis of the LULC change in the context of flood hazard suggests that most changes in LULC result in the conversion of wetland areas into developed areas and unplanned development in very high to moderate flood hazard zones. There was a 69% decrease in wetland and 94% increase in the developed area during the 35 years. While wetland was a primary land cover type in 1986, it became the least land cover type in 2020. These LULC changes could be responsible for the rise in flooding in the State.

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