scholarly journals Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA

2021 ◽  
Vol 13 (19) ◽  
pp. 3840
Author(s):  
Rowan L. Converse ◽  
Christopher D. Lippitt ◽  
Caitlin L. Lippitt
Keyword(s):  
New Mexico ◽  
Vegetation Dynamics ◽  
Vegetation Indices ◽  
Model Parameters ◽  
Drought Event ◽  
Soil Fractions ◽  
Western Us ◽  
Green Vegetation ◽  
Historic Data ◽  
Spectral Mixture

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2019 ◽  
pp. 1372-1382
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

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.

Using historic data to assess effectiveness of shrub removal in southern New Mexico

2005 ◽  
Vol 62 (1) ◽  
pp. 75-91 ◽  
Author(s):  
A. Rango ◽  
L. Huenneke ◽  
M. Buonopane ◽  
J.E. Herrick ◽  
K.M. Havstad
Keyword(s):  
New Mexico ◽  
Historic Data

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.

Global transpiration modelling: can the optimality hypothesis improve partitioning of ecosystem-scale evapotranspiration?

2020 ◽  
Author(s):  
Alison Prior ◽  
Iain-Colin Prentice
Keyword(s):  
Climate Models ◽  
Gross Primary Production ◽  
Vapour Pressure Deficit ◽  
Co2 Concentration ◽  
Water Model ◽  
Model Parameters ◽  
Water Runoff ◽  
Vegetation Growth ◽  
Green Vegetation ◽  
Surface Water Runoff

<p>The volume of water entering the atmosphere through transpiration is thought to be greater than the flow of all rivers to the oceans. It makes up the majority of evapotranspiration (ET) and significantly contributes to rainfall and therefore also to surface water runoff. However, there is no consensus on how transpiration responds to a changing environment; or even as to whether it is increasing over time. Global transpiration estimates are most commonly made through the partitioning of ET models.  However, in many ET models, the dynamics of vegetation growth and associated impacts on evapotranspiration are overlooked. Therefore, global estimates of transpiration from climate models are poorly constrained, with large uncertainties especially in stomatal conductance.</p><p>The ‘P model’ (for Production) is a recently developed, ‘next-generation’ model for Gross Primary Production, GPP. Derived from biochemical process of plants, the P model is built upon the established standard model for photosynthesis – combined with optimality hypotheses for the adaptation and acclimation of key model parameters – to determine GPP. The P model has the potential to provide a coupled global carbon and water model that responds correctly to changing environmental conditions. It requires only elevation, CO2 concentration, incident solar radiation, vapour pressure deficit (VPD) and temperature as inputs, in addition to remotely sensed green vegetation cover (fAPAR). The key idea motivating this research is that by exploiting the coupling of land-atmosphere carbon and water exchanges through stomatal behaviour, it should be possible to develop a near real-time transpiration monitoring system in which fAPAR is a key input. The P-model provides the means to do this. Initial results will be shown for both transpiration and GPP, with validation at >100 eddy-covariance flux-tower sites.</p>

scholarly journals Kajian Transformasi Indeks Vegetasi Citra Satelit Sentinel-2A untuk Estimasi Produksi Daun Kayu Putih Menggunakan Linear Spectral Mixture Analysis

2020 ◽  
Vol 3 (1) ◽  
pp. 63
Author(s):  
Lilik Norvi Purhartanto ◽  
Projo Danoedoro ◽  
Pramaditya Wicaksono
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Estimation Model ◽  
Leaf Production ◽  
Melaleuca Cajuputi ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

A forest plantation area of Melaleuca cajuputi at BDH Karangmojo, BKPH Yogyakarta are 2,325.20 ha. One of the efforts to keep its sustainability is to plan the target and realization of cajuputi leaf production considerwith forest condition. Advances in remote sensing technology can be an alternative in estimating the cajuputi leaf production on large areas with an efficient time and high accuracy and able to analyze the quality of cajuputi. This study aims to examine Sentinel-2A capabilities through a relationship model of some vegetation indices integrated with vegetative factors on the production to obtain estimates of leaf production, map and test the estimation model accuracy. The method used is to classify objects in pixels with Linear Spectral Mixture Analysis and build relationship between age, number of plants and vegetation index with cajuputi leaf production. The results showed that the unmixing method has 99,66% accuracy in classifying pixels into the fraction of cajuputi. MERIS Terrestrial Chlorophyll Index of unmixing cajuputi fraction simultaneously with age and number of plants has the highest correlation with value of r = 0,668 to the production and modeled in mapping the estimated cajuputi leaf production at the research location with Standard Error of Estimate is 0,183.

Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

2019 ◽  
Vol 57 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Jaebeom Kim ◽  
Sinkyu Kang ◽  
Bumsuk Seo ◽  
Amratuvshin Narantsetseg ◽  
Youngji Han
Keyword(s):  
Vegetation Cover ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Green Vegetation

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

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