scholarly journals New Biomass Estimates for Chaparral-Dominated Southern California Landscapes

2021 ◽  
Vol 13 (8) ◽  
pp. 1581
Author(s):  
Charlie C. Schrader-Patton ◽  
Emma C. Underwood
Keyword(s):  
Southern California ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Management Actions ◽  
Biomass Estimate ◽  
Resource Managers ◽  
Dynamic Landscape

Chaparral shrublands are the dominant wildland vegetation type in Southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrublands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict aboveground live biomass in Southern California using remote sensing data (Landsat Normalized Difference Vegetation Index (NDVI)) and a suite of geophysical variables. By substituting the NDVI and precipitation predictors for any given year, we were able to apply the model to each year from 2000 to 2019. Using a total of 980 field plots, our model had a k-fold cross-validation R2 of 0.51 and an RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE = 9.7) for Sierran mixed-conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers, we calculated the mean year 2010 shrubland biomasses for the four national forests that ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomasses from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measurements in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.

scholarly journals New Biomass Estimates for Chaparral-Dominated Southern California Landscapes

2021 ◽  
Author(s):  
Charlie Schrader-Patton ◽  
Emma C. Underwood
Keyword(s):  
Southern California ◽  
Vegetation Type ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Management Actions ◽  
Biomass Estimate ◽  
Resource Managers ◽  
Dynamic Landscape ◽  
The Mean ◽  
Field Plots

Chaparral shrublands are the dominant wildland vegetation type in southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrub-lands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict above ground live biomass in southern California using remote sensing data (Landsat NDVI) and a suite of geophysical variables. By substituting the NDVI and precipi-tation predictors for any given year we were able to apply the model to each year from 2000-2019. Using a total of 980 field plots, our model had a k-fold cross validation R2 of 0.51 and a RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE=9.7) for Sierran mixed conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over a two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers we calculated the mean year 2010 shrubland biomass for the four national forests which ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomass from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measure-ments in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.

scholarly journals CLASSIFYING AND MAPPING OF VEGETATED AREA IN AL- BAHA REGION, SAUDI ARABIA USING REMOTE SENSING. I. EXTENT AND DISTRIBUTION OF GROUND VEGETATED COVER CATEGORIES.

2020 ◽  
pp. 75-80
Author(s):  
Abdullah Saleh Al-Ghamdi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Vegetation Mapping ◽  
Primary Objective ◽  
High Density ◽  
Vegetation Density

Classifying and mapping vegetation is an important technical task for managing natural resources; the primary objective of the vegetation-mapping inventory is to produce high quality, standardized maps and associated data sets of vegetation. Satellite remote sensing has proven to be effective technology for mapping forest vegetation at the landscape to regional scale. In the remote sensing technique, vegetation density can be directly indicated by vegetation indices. Although there are several vegetation indices, the most widely used is the Normalized Difference Vegetation Index (NDVI), formulated by transforming raw satellite data into NDVI values, ranging from -1 to 1. NDVI enables the creation of images and other products that provide a rough measure of vegetation type, amount, and condition on land surfaces. The results show that medium to high density vegetation is mostly found in the central part of Al-Baha region separating the highlands and lowlands. The relationship study between NDVI and vegetation cover percentage in this study depicts an NDVI value of only 0.20–1.00, which indicates that vegetation covers over 60% of Al-Baha. This is probably because vegetation here may not only comprise trees but also other plant forms such as herbs and shrubs. However, only 862.5 km2 (7.7%) of Al-Baha is covered with medium-high density vegetation, found mainly at the 6 –15km width horizontal central belt (in the Al-Mandaq, Al-Baha, and south Baljurashi districts) along a high, foggy mountainous plateau. Conversely, about 65% of Al-Baha region has very low to no vegetation density; vegetation is found extensively in the Tihama low plain towards the Red Sea and in the north-eastern desert plain. This study has provided a comprehensive report on vegetation mapping in the Al-Baha region.

scholarly journals Soil Mycobiome Is Shaped by Vegetation and Microhabitats: A Regional-Scale Study in Southeastern Brazil

2021 ◽  
Vol 7 (8) ◽  
pp. 587
Author(s):  
Danielle Hamae Yamauchi ◽  
Hans Garcia Garces ◽  
Marcus de Melo Teixeira ◽  
Gabriel Fellipe Barros Rodrigues ◽  
Leila Sabrina Ullmann ◽  
...  
Keyword(s):  
Fungal Community ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Deciduous Forest ◽  
Regional Scale ◽  
Southeastern Brazil ◽  
Transitional Region ◽  
Illumina Platform ◽  
Ecological Processes ◽  
Life On Earth

Soil is the principal habitat and reservoir of fungi that act on ecological processes vital for life on Earth. Understanding soil fungal community structures and the patterns of species distribution is crucial, considering climatic change and the increasing anthropic impacts affecting nature. We evaluated the soil fungal diversity in southeastern Brazil, in a transitional region that harbors patches of distinct biomes and ecoregions. The samples originated from eight habitats, namely: semi-deciduous forest, Brazilian savanna, pasture, coffee and sugarcane plantation, abandoned buildings, owls’ and armadillos’ burrows. Forty-four soil samples collected in two periods were evaluated by metagenomic approaches, focusing on the high-throughput DNA sequencing of the ITS2 rDNA region in the Illumina platform. Normalized difference vegetation index (NDVI) was used for vegetation cover analysis. NDVI values showed a linear relationship with both diversity and richness, reinforcing the importance of a healthy vegetation for the establishment of a diverse and complex fungal community. The owls’ burrows presented a peculiar fungal composition, including high rates of Onygenales, commonly associated with keratinous animal wastes, and Trichosporonales, a group of basidiomycetous yeasts. Levels of organic matter and copper influenced all guild communities analyzed, supporting them as important drivers in shaping the fungal communities’ structures.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Alpine sparsely vegetated areas in the eastern Qilian Mountains shrank with climate warming in the past 30 years

2018 ◽  
Vol 42 (4) ◽  
pp. 415-430 ◽  
Author(s):  
Biao Zeng ◽  
Fuguang Zhang ◽  
Taibao Yang ◽  
Jiaguo Qi ◽  
Mihretab G Ghebrezgabher
Keyword(s):  
Climate Change ◽  
Climate Warming ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Qilian Mountains ◽  
Visual Interpretation ◽  
Hydrothermal Conditions ◽  
The Past ◽  
Distribution Ranges

Alpine sparsely vegetated areas (ASVAs) in mountains are sensitive to climate change and rarely studied. In this study, we focused on the response of ASVA distribution to climate change in the eastern Qilian Mountains (EQLM) from the 1990s to the 2010s. The ASVA distribution ranges in the EQLM during the past three decades were obtained from the Thematic Mapper remote sensing digital images by using the threshold of normalized difference vegetation index (NDVI) and artificial visual interpretation. Results indicated that the ASVA shrank gradually in the EQLM and lost its area by approximately 11.4% from the 1990s to the 2010s. The shrunken ASVA with markedly more area than the expanded one was mainly located at altitudes from 3700 m to 4300 m, which were comparatively lower than the average altitude of the ASVA distribution ranges. This condition led to the low ASVA boundaries in the EQLM moving upwards at a significant velocity of 22 m/decade at the regional scale. This vertical zonal process was modulated by topography-induced differences in local hydrothermal conditions. Thus, the ASVA shrank mainly in its lower parts with mild and sunny slopes. Annual maximum NDVI in the transition zone increased significantly and showed a stronger positive correlation with significantly increasing temperature than insignificant precipitation variations during 1990–2015. The ASVA shrinkage and up-shifting of its boundary were attributed to climate warming, which facilitated the upper part of alpine meadow in the EQLM by releasing the low temperature limitation on vegetation growth.

scholarly journals Does EO NDVI seasonal metrics capture variations in species composition and biomass due to grazing in semi-arid grassland savannas?

2015 ◽  
Vol 12 (14) ◽  
pp. 4407-4419 ◽  
Author(s):  
J. L. Olsen ◽  
S. Miehe ◽  
P. Ceccato ◽  
R. Fensholt
Keyword(s):  
Time Series ◽  
Species Composition ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Grazing Intensity ◽  
Test Site ◽  
Grazing Pressure ◽  
Modis Ndvi ◽  
Coarse Resolution

Abstract. Most regional scale studies of vegetation in the Sahel have been based on Earth observation (EO) imagery due to the limited number of sites providing continuous and long term in situ meteorological and vegetation measurements. From a long time series of coarse resolution normalized difference vegetation index (NDVI) data a greening of the Sahel since the 1980s has been identified. However, it is poorly understood how commonly applied remote sensing techniques reflect the influence of extensive grazing (and changes in grazing pressure) on natural rangeland vegetation. This paper analyses the time series of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI metrics by comparing it with data from the Widou Thiengoly test site in northern Senegal. Field data include grazing intensity, end of season standing biomass (ESSB) and species composition from sizeable areas suitable for comparison with moderate – coarse resolution satellite imagery. It is shown that sampling plots excluded from grazing have a different species composition characterized by a longer growth cycle as compared to plots under controlled grazing or communal grazing. Also substantially higher ESSB is observed for grazing exclosures as compared to grazed areas, substantially exceeding the amount of biomass expected to be ingested by livestock for this area. The seasonal integrated NDVI (NDVI small integral; capturing only the signal inherent to the growing season recurrent vegetation), derived using absolute thresholds to estimate start and end of growing seasons, is identified as the metric most strongly related to ESSB for all grazing regimes. However plot-pixel comparisons demonstrate how the NDVI/ESSB relationship changes due to grazing-induced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots. Hence, average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference. This indicates that a grazing-induced development towards less ESSB and shorter-cycled annual plants with reduced ability to turn additional water in wet years into biomass is not adequately captured by seasonal NDVI metrics.

Predicting spatial patterns of fire on a southern California landscape

2008 ◽  
Vol 17 (5) ◽  
pp. 602 ◽  
Author(s):  
Alexandra D. Syphard ◽  
Volker C. Radeloff ◽  
Nicholas S. Keuler ◽  
Robert S. Taylor ◽  
Todd J. Hawbaker ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Southern California ◽  
Vegetation Type ◽  
Fire Hazard ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Fuel Loading ◽  
Landscape Characteristics ◽  
Management Actions ◽  
Fire Ignitions

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

2012 ◽  
Vol 23 (2) ◽  
pp. 139-172
Author(s):  
Abdullah Salman Alsalman Abdullah Salman Alsalman
Keyword(s):  
Image Processing ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Test Area ◽  
Image Differencing ◽  
Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

scholarly journals Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

2020 ◽  
Vol 12 (10) ◽  
pp. 1546 ◽  
Author(s):  
Christopher Potter ◽  
Olivia Alexander
Keyword(s):  
Time Series ◽  
Coastal Plain ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Vegetation Growth ◽  
The Past ◽  
Moderate Resolution Imaging Spectroradiometer

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

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