Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes

Leong, Misha; Roderick, George

doi:10.7717/peerj.1141

Cited by 28 publications

(35 citation statements)

References 43 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Resource availability across landscapes, however, is often seasonally variable (Kremen ). Examining NDVI (normalized difference vegetation index), Leong and Roderick () found that urban, agricultural, and natural areas provided pulses of floral resources at different times of year. Further, pollinator abundance tracked these changes in resource availability.…”

Section: Discussionmentioning

confidence: 99%

Sunflower (Helianthus annuus) pollination in California's Central Valley is limited by native bee nest site location

Sardiñas

Tom

Ponisio

et al. 2016

Ecological Applications

View full text Add to dashboard Cite

A major challenge in habitat restoration is targeting the key aspects of a species' niche for enhancement, particularly for species that use a diverse set of habitat features. However, restoration that focuses on limited aspects of a species' niche may neglect other resources that are critical to population persistence. We evaluated the ability of native plant hedgerows, planted to increase pollen and nectar resources for wild bees in agricultural landscapes, to provide suitable nesting habitat and enhance nesting rates of ground-nesting bees. We found that, when compared to unmanaged field edges (controls), hedgerows did not augment most indicators of nest habitat quality (bare ground, soil surface irregularity, and soil hardness), although coarser soils were associated with higher incidence and richness of nesting bees. Hedgerows did not augment nesting rates when compared to control edges. Although all the bee species we detected nesting were also found foraging on floral resources, the foraging versus nesting assemblages found within a site were highly dissimilar. These results may reflect sampling error; or, species found foraging but not nesting in hedgerows could be utilizing hedgerows as "partial habitats," nesting outside hedgerow plantings but foraging on the floral resources they provide. We conclude that although hedgerows are known to provide critical floral resources to wild bees especially in resource-poor intensive agricultural landscapes, simply increasing vegetative diversity and structure may not be simultaneously enhancing nesting habitat for ground-nesting bees.

show abstract

Section: Discussionmentioning

confidence: 99%

Sunflower (Helianthus annuus) pollination in California's Central Valley is limited by native bee nest site location

Sardiñas

Tom

Ponisio

et al. 2016

Ecological Applications

View full text Add to dashboard Cite

show abstract

“…Because town areas have less green space and are mostly ornamental plants, anthropogenic supplements of water and nutrients could prolong the vegetation growing season. It causes the growth of vegetation to be relatively stable [65]. Meanwhile, several studies demonstrated that anthropogenic disturbances can assist in the selection of more productive ecosystems [32].…”

Section: Disturbance Of Anthropogenic Factors On Vegetation Greening mentioning

confidence: 99%

Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers

et al. 2018

View full text Add to dashboard Cite

The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

show abstract

“…2. Remote sensing data such NDVI often reflect human-altered vegetation patterns and ought not be used as covariates in PNV mapping (Leong and Roderick, 2015).…”

Section: /35mentioning

confidence: 99%

Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential

Hengl¹,

Walsh

Sanderman

et al. 2018

Preprint

View full text Add to dashboard Cite

Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing Machine Learning Algorithms (MLA) for operational mapping of Potential Natural Vegetation (PNV). The following MLA were considered: neural networks (nnet package), random forest (ranger), gradient boosting (gmb), K-nearest neighborhood (class) and cubist. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief and lithologic variables, were used as explanatory variables. The overall results show that random forest gives the overall best performance. he highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial Cross Validation) and 68% (simple random subsetting), with the most important predictors being total annual precipitation, monthly temperatures and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers and month of the year, respectively. Further developments of PNV mapping could include using GBIF records to map global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at 1 km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.

show abstract

Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes

Cited by 28 publications

References 43 publications

Sunflower (Helianthus annuus) pollination in California's Central Valley is limited by native bee nest site location

Sunflower (Helianthus annuus) pollination in California's Central Valley is limited by native bee nest site location

Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers

Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential

Contact Info

Product

Resources

About