Browning of the landscape of interior Alaska based on 1986-2009 Landsat sensor NDVI

2012 ◽  
Vol 42 (7) ◽  
pp. 1371-1382 ◽  
Author(s):  
Rebecca A. Baird ◽  
David Verbyla ◽  
Teresa N. Hollingsworth
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Black Spruce ◽  
Spatial Scales ◽  
Sensor Data ◽  
Populus Balsamifera ◽  
Interior Alaska ◽  
Climatic Regime ◽  
North And South

We used a time series of 1986–2009 Landsat sensor data to compute the Normalized Difference Vegetation Index (NDVI) for 30 m pixels within the Bonanza Creek Experimental Forest of interior Alaska. Based on simple linear regression, we found significant (p < 0.05) declining trends in mean NDVI of three dominant landscape types of floodplains, lowlands, and uplands. At smaller patch sizes, similar declining trends occurred among topographic classes of north- and south-facing slopes and valley bottoms and among forest classes, including black spruce (Picea mariana (Mill.) B.S.P.). Significant positive trends in mean NDVI occurred only in areas that were recently burned, whereas wetlands had no significant trend. The greatest departure from the NDVI trend line occurred following the 2004 drought for all forest classes except black spruce, which dominates the coldest sites, and balsam poplar (Populus balsamifera L.), which occurs on low, moist terraces within the Tanana River floodplain. The consistent long-term declining trend at several spatial scales may be due to a regional climatic regime shift that occurred in the mid-1970s.

scholarly journals Forage Production of the Argentine Pampa Region Based on Land Use and Long-Term Normalized Difference Vegetation Index Data

2009 ◽  
Vol 62 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Carlos M. Di Bella ◽  
Ignacio J. Negri ◽  
Gabriela Posse ◽  
Florencia R. Jaimes ◽  
Esteban G. Jobbágy ◽  
...  
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forage Production ◽  
Index Data ◽  
Pampa Region

scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

Inter-comparison of remotely-sensed actual evapotranspiration products in the Zayandehrud river basin, Iran

2021 ◽  
Author(s):  
Neda Abbasi ◽  
Hamideh Nouri ◽  
Sattar Chavoshi Borujeni ◽  
Pamela Nagler ◽  
Christian Opp ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Water Shortage ◽  
Remotely Sensed ◽  
Irrigation Scheduling ◽  
Accurate Estimation ◽  
Agricultural Areas ◽  
Eta Products

&lt;p&gt;Accurate estimation of evapotranspiration (ET) helps to create a better understanding of water allocation, irrigation scheduling, and crop management especially in arid and semiarid regions where agricultural areas are far more affected by water shortage and drought events. Remote sensing (RS) facilitates estimating the ET in regions where long-term field measurements are missed.&amp;#160; In this study, we compare the performance of free open-access remotely sensed actual ET products at eleven counties of the Zayandehrud basin. The Zayandehrud basin, one of the major watersheds of Iran, suffers from recurrent droughts and long-term impacts of aridity. The RS products used in this study are namely WaPOR (2009-2019), MOD16A2 (2003-2019), SSEBOp (2003-2019). We also merged the two products of SSEBOp and WaPOR and assessed its performance. To prepare the Merged ETa Product (MEP), WaPOR was resampled to the spatial resolution of SSEBOp. Then, the average pixel values of the resampled ETa product and SSEBOp were calculated. To compare ETa estimations over croplands in each county, maximum Normalized Difference Vegetation Index (NDVI) maps at annual scale (2003-2019) were prepared using LANDSAT 5, 7, and 8 images. Annual mean ETa estimations were then extracted over croplands by using annual maximum NDVI layers. We compared the RS-based ETa with reported long-term ETa values extracted from the local available literature. Our results showed a consistent underestimation of MOD16A2 in all counties. The MEP and WaPOR outperformed other products in the estimation of ETa in seven. Estimations of WaPOR and SSEBOp agreed in most of the counties. Our analysis displayed that, although MOD16A2 underestimated ETa values, it could together with SSEBOp capture the drought better than that of WaPOR and MEP in the lower reaches of the basin. Further study is needed to evaluate the monthly and seasonal performance of RS-based ETa products.&lt;/p&gt;

scholarly journals Detecting Vegetation Recovery after Fire in A Fire-Frequented Habitat Using Normalized Difference Vegetation Index (NDVI)

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 749
Author(s):  
Danielle L. Lacouture ◽  
Eben N. Broadbent ◽  
Raelene M. Crandall
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Vegetation Recovery ◽  
Growing Seasons ◽  
Different Seasons ◽  
Dormant Season ◽  
In Fire ◽  
Subtropical Savanna

Research Highlights: Fire-frequented savannas are dominated by plant species that regrow quickly following fires that mainly burn through the understory. To detect post-fire vegetation recovery in these ecosystems, particularly during warm, rainy seasons, data are needed on a small, temporal scale. In the past, the measurement of vegetation regrowth in fire-frequented systems has been labor-intensive, but with the availability of daily satellite imagery, it should be possible to easily determine vegetation recovery on a small timescale using Normalized Difference Vegetation Index (NDVI) in ecosystems with a sparse overstory. Background and Objectives: We explore whether it is possible to use NDVI calculated from satellite imagery to detect time-to-vegetation recovery. Additionally, we determine the time-to-vegetation recovery after fires in different seasons. This represents one of very few studies that have used satellite imagery to examine vegetation recovery after fire in southeastern U.S.A. pine savannas. We test the efficacy of using this method by examining whether there are detectable differences between time-to-vegetation recovery in subtropical savannas burned during different seasons. Materials and Methods: NDVI was calculated from satellite imagery approximately monthly over two years in a subtropical savanna with units burned during dry, dormant and wet, growing seasons. Results: Despite the availability of daily satellite images, we were unable to precisely determine when vegetation recovered, because clouds frequently obscured our range of interest. We found that, in general, vegetation recovered in less time after fire during the wet, growing, as compared to dry, dormant, season, albeit there were some discrepancies in our results. Although these general patterns were clear, variation in fire heterogeneity and canopy type and cover skewed NDVI in some units. Conclusions: Although there are some challenges to using satellite-derived NDVI, the availability of satellite imagery continues to improve on both temporal and spatial scales, which should allow us to continue finding new and efficient ways to monitor and model forests in the future.

scholarly journals Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

2020 ◽  
Vol 12 (1) ◽  
pp. 190 ◽  
Author(s):  
Ruyin Cao ◽  
Yan Feng ◽  
Xilong Liu ◽  
Miaogen Shen ◽  
Ji Zhou
Keyword(s):  
Western Europe ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
High Latitudes ◽  
Large Spatial Scale ◽  
Arid Grasslands ◽  
Seasonal Snow Cover ◽  
Spring Snowmelt

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

Long-term monitoring of climatic and nutritional affects on tree growth in interior Alaska

2010 ◽  
Vol 40 (7) ◽  
pp. 1325-1335 ◽  
Author(s):  
J. Yarie ◽  
K. Van Cleve
Keyword(s):  
Tree Growth ◽  
Black Spruce ◽  
Growth Period ◽  
Early Spring ◽  
Large Set ◽  
Primary Control ◽  
Forest Types ◽  
Interior Alaska ◽  
State Factor

The comparative analysis of a large set of long-term fertilization and thinning studies in the major forest types of interior Alaska is summarized. Results indicate that nutrient limitations may only occur during the early spring growth period, after which moisture availability is the primary control of tree growth on warm sites. The temperature dynamics of both air and soil set seasonal bounds on the nutrient and moisture dynamics for all forest types. Air and soil temperature limitations are the primary control of intraseasonal growth in the colder topographic locations in interior Alaska. These locations are usually dominated by black spruce (Picea mariana (Mill.) Britton, Sterns, Poggenb.) vegetation types. The seasonal progression of factors controlling growth is strongly tied to the state factor structure of the landscape.

scholarly journals Phenological behavior of Atlas cedar (Cedrus atlantica) forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Khaled Missaoui ◽  
Rachid Gharzouli ◽  
Yamna Djellouli ◽  
Frençois Messner
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Precipitation Variability ◽  
Monthly Precipitation ◽  
Cedrus Atlantica ◽  
The Past

Abstract. Missaoui K, Gharzouli R, Djellouli Y, Messner F. 2020. Phenological behavior of Atlas cedar (Cedrus atlantica)  forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria. Biodiversitas 21: 239-245. Understanding the changes in snow and precipitation variability and how forest vegetation response to such changes is very important to maintain the long-term sustainability of the forest. However, relatively few studies have investigated this phenomenon in Algeria. This study was aimed to find out the response of Atlas cedar (Cedrus atlantica (Endl.) G.Manetti ex Carrière) forest in two areas (i.e Boutaleb and Babors Mountains) and their response to the precipitation and snow variability. The normalized difference vegetation index (NDVI) generated from satellite images of MODIS time series was used to survey the changes of the Atlas cedar throughout the study area well as dataset of monthly precipitation and snow of the province of Setif (northeast of Algeria) from 2000 to 2018. Descriptive analysis using Standarized Precipitation Index (SPI) showed the wetter years were more frequent in the past than in the last two decades. The NDVI values changes in both areas with high values were detected in Babors Mountains with statistically significant differences. Our findings showed important difference in Atlas cedar phenology from Boutaleb mountains to Babors Mountains which likely related to snow factor.

scholarly journals Pemetaan Vegetasi Hutan Mangrove Menggunakan Metode Normalized Difference Vegetation Index (NDVI) di Desa Arakan, Minahasa Selatan, Sulawesi Utara. SOIJST Vol. 1 (2):211-222

2018 ◽  
Author(s):  
Intan Philiani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Digital Signal ◽  
Sensor Data ◽  
Infrared Band ◽  
Image Recording ◽  
Band Combination ◽  
Satellite Sensor Data ◽  
The Village

Tatapaan District in North Minahasa has mangrove forest covering an area of 8.736.00 m2. One of the village in Tatapaan District is Arakan. This study aim for mapping mangrove density in Arakan village and determine the best result of Normalized Difference Vegetation Index (NDVI) from band combination used. NDVI method calculate the amount of vegetation greeness value derived from digital signal processing of brightness value data of multiple channels satellite sensor data from satellite images. NDVI measures the slope between the original value of the red band and infrared band in the sky with the value of each pixel in the image. Imagery used is Worldview2 satellite image recording on June 19th 2014. Based on the combination of bands used, the best result of band combination is the combination of Red and NIR 2 band with the value of the smallest error rate of deviation, ie 0.3. The density of the widest is “Rapat” class (824,566.01 m2), “Sedang” class (133,622.41 m2), “Jarang” class (12,004.92 m2), “Sangat jarang” class (10,494.23 m2), and the smallest is “Sangat rapat” class (24.45 m2).

scholarly journals INVESTIGATING MANGROVE FRAGMENTATION CHANGES USING LANDSCAPE METRICS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 159-162
Author(s):  
M. Baharlouii ◽  
D. Mafi Gholami ◽  
M. Abbasi
Keyword(s):  
Climate Change ◽  
Landscape Metrics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Images ◽  
Surrounding Water ◽  
Maximum Likelihood Classification ◽  
High Degree ◽  
Drought Occurrence

Abstract. Generally, investigation of long-term mangroves fragmentation changes can be used as an important tool in assessing sensitivity and vulnerability of these ecosystems to the multiple environmental hazards. Therefore, the aim of this study was to reveal the trend of mangroves fragmentation changes in Khamir habitat using satellite imagery and Fragstats software during a 30-year period (1986–2016). To this end, Landsat images of 1986, 1998, and 2016 were used and after computing the normalized difference vegetation index (NDVI) to distinguish mangroves from surrounding water and land areas, images were further processed and classified into two types of land cover (i.e., mangrove and non-mangrove areas) using the maximum likelihood classification method. By determining the extent of mangroves in the Khamir habitat in the years of 1986, 1998 and 2017, the trend of fragmentation changes was quantified using CA, NP, PD and LPI landscape metrics. The results showed that the extent of mangroves in Khamir habitat (CA) decreased in the period post-1998 (1998–2016). The results also showed that, the NP and PD increased in the period of post-1998 and in contrast, the LPI decrease in this period. These results revealed the high degree of vulnerability of mangroves in Khamir habitat to the drought occurrence and are thus threatened by climate change. We hope that the results of this study stimulate further climate change adaptation planning efforts and help decision-makers prioritize and implement conservative measures in the mangrove ecosystems on the northern coasts of the PG and the GO and elsewhere.

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