Are plant communities mainly determined by anthropogenic land cover along urban riparian corridors?

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.

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Riparian forest structure and stream geomorphic condition: implications for flood resilience

Canadian Journal of Forest Research ◽

10.1139/cjfr-2016-0327 ◽

2017 ◽

Vol 47 (4) ◽

pp. 476-487 ◽

Cited By ~ 5

Author(s):

William S. Keeton ◽

Erin M. Copeland ◽

S. Mažeika P. Sullivan ◽

Mary C. Watzin

Keyword(s):

Land Cover ◽

Forest Structure ◽

Forest Cover ◽

Linear Models ◽

Riparian Forest ◽

Spatial Scales ◽

Basal Area ◽

Riparian Corridors ◽

Stream Condition ◽

Stream Geomorphology

Managing riparian corridors for flood resilience requires understanding of linkages between vegetation condition and stream geomorphology. Stream assessment approaches increasingly use channel morphology as an indicator of stream condition, with only cursory examination of riparian vegetation. Our research (i) examines relationships between stream geomorphic condition, as assessed by Rapid Geomorphic Assessment (RGA) scores, and riparian forest structure, and (ii) investigates scale dependencies in the linkages between land cover and stream geomorphology. We sampled vegetation structure and composition and assessed geomorphic condition at 32 stream reaches within the Lake Champlain Basin, USA. RGA scores were modeled as a function of structural attributes using classification and regression trees. Landsat coverages were used to delineate land uses within five nested spatial scales. Generalized linear models (GLM) evaluated relationships between land cover and RGA scores. Standard deviation of basal area partitioned the greatest variability in RGA scores, but dead tree density and basal area (positively) and shrub density (negatively) were also significant predictors. RGA was related to forest and agricultural cover at the two finest scales. Riparian forest structure is highly dynamic in relation to stand development and disturbance history; simple forest cover information does not capture these differences or their influences on stream geomorphic condition.

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High-resolution land-use land-cover change data for regional climate modelling applications over Europe – Part 1: The plant functional type basemap for 2015

10.5194/essd-2021-251 ◽

2021 ◽

Author(s):

Vanessa Reinhart ◽

Peter Hoffmann ◽

Diana Rechid ◽

Jürgen Böhner ◽

Benjamin Bechtel

Keyword(s):

Land Use ◽

High Resolution ◽

Land Cover ◽

Plant Communities ◽

Regional Climate ◽

Climate Modelling ◽

Climate Information ◽

Climate Data ◽

Lulc Change ◽

European Continent

Abstract. The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset forEurope (LANDMATE PFT Version 1.0 Reinhart et al., 2021b, ;). The dataset is based on the high-resolution ESA-CCI land cover dataset and is further improved through the the additional use of climate information. Within the LANDMATE PFT dataset, satellite-based LULC information and climate data are combined to achieve the best possible representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge Life Zone concept. Through the consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European Continent. A suitable evaluation method has been developed and applied to assess the quality of thenew PFT dataset. The assessment shows that the dominant LULC groups, cropland and woodland, are well represented within the dataset while uncertainties are found for some less represented LULC groups. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as basis for the LUCAS LUC dataset introduced in the companion paper by Hoffmann et al. (submitted) which is available for use as LULC change input for RCM experiment setups focused on investigating LULC change impact.

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The Application of Decision Binary Trees to Assess the Usefulness of The Digital Terrain Model in Studying the Relationships Between Relief and Vegetation in The Polish High Tatra

Miscellanea Geographica ◽

10.2478/mgrsd-2006-0035 ◽

2006 ◽

Vol 12 (1) ◽

pp. 305-313

Author(s):

Karol Kącki

Keyword(s):

Land Cover ◽

Plant Communities ◽

Natural Environment ◽

Dominant Role ◽

Digital Terrain Model ◽

Binary Trees ◽

Terrain Model ◽

Digital Terrain ◽

The Subject ◽

The One

Abstract The relationships between individual components of the natural environment have long been an object of research (Kostrowicki, Wójcik, 1972; Rączkowska, Kozłowska, 1994; Kozłowska, Rączkowska, 1996). This paper is an attempt to analyse the relationships between two geocomponents of the natural environment: relief and vegetation, from a perspective contrary to the one currently prevailing in the literature of the subject. This approach assumes that relief, with its dominant role as a component strongly affecting the formation of the remaining factors, can be indicative in character and as such can represent basic factors that help determine and anticipate the occurrences of certain plant communities as well as locations with no vegetation. Using geoinformation data along with the tools to process them, an attempt was made to assess the usefulness of the DTM (Digital Terrain Model) to identify selected plant communities, rock and water. The development of a model of the relationships between the relief and the vegetation is an attempt to capture the correspondence between the parameters characterising the relief, calculated using the DTM model and classes of objects, with the use of information obtained from an Ikonos XS image. This model was subsequently used to draw a map of the land cover for a part of the Gąsienicowa Valley in the High Tatra (Dolina Gąsienicowa). For the purpose of this exercise, a technique of data classification called DBT (Decision Binary Trees) was used.

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