Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model

2017 ◽  
Vol 197 ◽  
pp. 98-114 ◽  
Author(s):  
Jinhu Bian ◽  
Ainong Li ◽  
Zhengjian Zhang ◽  
Wei Zhao ◽  
Guangbin Lei ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Alpine Wetland ◽  
Green Vegetation

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

scholarly journals Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

2019 ◽  
Vol 11 (24) ◽  
pp. 2963
Author(s):  
Christopher L. Kibler ◽  
Anne-Marie L. Parkinson ◽  
Seth H. Peterson ◽  
Dar A. Roberts ◽  
Carla M. D’Antonio ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Species Composition ◽  
Vegetation Cover ◽  
Spatial And Temporal Variation ◽  
Conifer Species ◽  
Burn Scar ◽  
Green Vegetation ◽  
Normalized Burn Ratio

Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011–2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Biophsyical constraints on gross primary production by the terrestrial biosphere

2014 ◽  
Vol 11 (20) ◽  
pp. 5987-6001 ◽  
Author(s):  
H. Wang ◽  
I. C. Prentice ◽  
T. W. Davis
Keyword(s):  
Primary Production ◽  
Optimality Theory ◽  
Vegetation Cover ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Water Shortage ◽  
C3 Plants ◽  
Environmental Controls ◽  
Green Vegetation

Abstract. Persistent divergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, Δ E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.

scholarly journals Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

2018 ◽  
Vol 56 (3) ◽  
pp. 362-387 ◽  
Author(s):  
Noa Ohana-Levi ◽  
Tarin Paz-Kagan ◽  
Natalya Panov ◽  
Aviva Peeters ◽  
Asaf Tsoar ◽  
...  
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Time Series Analysis ◽  
Vegetation Cover ◽  
Residential Development ◽  
Series Analysis

scholarly journals Green Vegetation Cover Has Steadily Increased since Establishment of Community Forests in Western Chitwan, Nepal

2020 ◽  
Vol 12 (24) ◽  
pp. 4071
Author(s):  
Jie Dai ◽  
Dar A. Roberts ◽  
Douglas A. Stow ◽  
Li An ◽  
Qunshan Zhao
Keyword(s):  
Vegetation Cover ◽  
Vegetation Dynamics ◽  
Forest Cover ◽  
Local Community ◽  
Sustainable Forest Management ◽  
Community Forestry ◽  
Mammal Species ◽  
Community Forests ◽  
Green Vegetation ◽  
Local Residents

Community forests have been established worldwide to sustainably manage forest ecosystem services while maintaining the livelihoods of local residents. The Chitwan National Park in Nepal is a world-renowned biodiversity hotspot, where community forests were consolidated in the park’s buffer zone after 1993. These western Chitwan community forests stand as the frontiers of human–environment interactions, nurturing endangered large mammal species while providing significant natural resources for local residents. Nevertheless, no systematic forest cover assessment has been conducted for these forests since their establishment. In this study, we examined the green vegetation dynamics of these community forests for the years 1988–2018 using Landsat surface reflectance products. Combining an automatic water extraction index, spectral mixture analysis and the normalized difference fraction index (NDFI), we developed water masks and quantified the water-adjusted green vegetation fractions and NDFI values in the forests. Results showed that all forests have been continuously greening up since their establishment, and the average green vegetation cover of all forests increased from approximately 30% in 1988 to above 70% in 2018. With possible contributions from the invasion of exotic understory plant species, we credit community forestry programs for some of the green-up signals. Monitoring of forest vegetation dynamics is critical for evaluating the effectiveness of community forestry as well as developing sustainable forest management policies. Our research will provide positive feedbacks to local community forest committees and users.

Sign in / Sign up

Export Citation Format

Share Document