Top PDF Modeling forest canopy closure using vegetation index

Modeling forest canopy closure using vegetation index

Modeling forest canopy closure using vegetation index

The use of remote sensing to estimate forest structure has been largely tested but few researches have related such information about canopy closure in tropical forests in steep slope. This work aims to analyze the potential employ of vegetation index and sunlight radiation to generate a predictive model of canopy closure in the Ribeira Valley, south of the São Paulo State – Brazil. The canopy closure data were obtained from Spherical Densiometer in 52 sample points. The sun radiation (AIF) was obtained using TOPODATA image and literature equations. Thus, canopy closure and AIF information were related to NDVI, EVI and LAI, obtained from LANDSAT-TM and ALOS-AVNIR images. The results showed: a) field canopy closure facing to the North, East and West presented a tendency to have higher canopy closure then points facing to the south; b) the field canopy closure ranged from 0.58 to 0.97 c) and the annual illumination factor (AIF) ranging from 0.28 to 0.66 ; d) all three indexes showed lower determination coefficients when compared with image bands alone; e) two spatial resolution images were tested using 30 m (TM) and 10 m (ALOS); the lower pixel size did not result in better canopy closure estimation; f) the use of topographic correction on the TM images did not resulted in better model explanation of canopy closure, comparing it with models that use AIF; g) the blue ALOS band and TM7 Landsat band models explained, about 27% and 30% of the variation in observed canopy closure, respectively.
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Modeling and Mapping Forest Floor Distributions of Common Bryophytes Using a LiDAR Derived Depth to Water Index

Modeling and Mapping Forest Floor Distributions of Common Bryophytes Using a LiDAR Derived Depth to Water Index

The softwood stands surveyed generally had abundant bryophyte mats, thereby rendering SW to be the dominant vegetation type variable to predict the occur- rence of DP , PS and SG . In general, SG is associated with wet coniferous forests dominated by black spruce ( Picea mariana ), tamarack ( Larix laricina ), and bal- sam fir ( Abies balsamea ). In contrast, PS and DP are mostly associated with up- land forests dominated by red spruce ( Picea rubens ), eastern hemlock ( Tsuga canadensis ), balsam fir, and white pine ( Pinus strobus ). PS and DP were also correlated with the MX variable, which represents early to late successional mixedwood forests dominated by red maple ( Acer rubrum ), white birch ( Betula papyrifera ), and balsam fir, and later successional stages composed of yellow birch ( Betula alleghaniensis ), red spruce or eastern hemlock. Within these for- ests, ground flora is generally herbaceous and accompanied with bryophyte di- versity [25]. In pure hardwood stands, ground flora is dominated by shrubs and ferns. Bryophytes, when present, are generally constrained to grow on ex- posed coarse woody debris, likely due to moss-suppressing leaf litter cover elsewhere [25].
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Narrowband Vegetation Indices for Estimating Boreal Forest Leaf Area Index

Narrowband Vegetation Indices for Estimating Boreal Forest Leaf Area Index

The NIR and SWIR spectral bands were particularly important when all sample plots were analyzed together. This is in agreement with the best broadband indices, ISR and RSR. The importance of NIR and SWIR bands has been emphasized also in previous studies testing narrowband VIs for estimating forest LAI (e.g. Lee et al., 2004; Schlerf et al., 2005). The leaf (needle) reflectance at those wavelengths is mainly controlled by water absorption, although leaf biochemical components such as proteins, cellulose and lignin also contribute to absorption in the infrared (e.g. Curran, 1989). The amount of water at the canopy level is directly related to LAI, which explains strong correlations. The bands centered at 1134 nm and 1790 nm are among the Hyperion bands, which are closest to the water absorption regions centered at approximately 1200 nm and 1940 nm. The spectral bands close to the water absorption regions at 970 nm and 1400 nm are also employed in some of the best indices. The spectral bands of the broadband sensors are usually placed in the middle of the atmospheric windows to avoid atmospheric absorption. However, it seems that narrow spectral bands close to the water absorption regions are particularly interesting for estimating LAI. In these wavelength regions, the reflectance seems to be relatively insensitive to tree species or composition of the understory vegetation, as suggested earlier by the studies using broadband indices (e.g. Brown et al., 2000).
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Using Leaf-off LiDAR in Modeling Forest Canopy Structure and Assessing the Effect of Spatial Resolution in Landscape Analyses

Using Leaf-off LiDAR in Modeling Forest Canopy Structure and Assessing the Effect of Spatial Resolution in Landscape Analyses

The first two objectives of this research focus on modeling forest canopy structure. Measurements of forest canopy characteristics have important uses in forest research and management. The structure of a forest canopy strongly influences the ecological processes occurring in the understory, such as through its influence on understory light availability (Dreiss and Volin 2013; Canham et al. 1994; Pacala et al. 1994). In addition, canopy characteristics are correlated with forest biomass, tree density, habitat quality, and other stand conditions (Smith et al. 2008). Canopy height and closure are metrics commonly used in forest research; however, field techniques for measuring canopy height and closure have limitations that make it difficult to collect accurately large numbers of field observations. Heights are difficult to measure for leaning trees or for trees where the top of the crown is not easily visible. Canopy closure requires the use of hemispherical photography or quantum sensors that are best used under diffuse light conditions which occur at dawn, dusk, or under uniformly overcast skies (Anderson 1964; Rich 1989; Whitmore et al. 1993). While leaf-on LiDAR has been used in recent years to provide continuous and accurate information on canopy structure, research on the use of leaf-off LiDAR in this regard has been limited. Leaf-off LiDAR are optimal for modeling terrain which is one of the primary applications of LiDAR. Thus, leaf-off LiDAR are much more widely available than leaf-on data and leveraging these data for forest structure research would be highly
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Forest Canopy Density Mapping Using Advance Geospatial Technique

Forest Canopy Density Mapping Using Advance Geospatial Technique

In the field of Remote sensing spectral response green vegetation (healthy vegetation) is characterise by absorption and reflection of wavelength. The visible regions characterise by low reflectance due to pigment absorption and IR region having high reflectance is controlled by cell structure of leaf. The healthy vegetations are absorbed red band and more reflect the Infrared band of wave length. By using this advantage the scientific ratio index (Normalized Difference Vegetation Index) technique we explore the healthy vegetations. Another most popular vegetation index is advance Vegetation Index (AVI).
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Modeling of light transmission under heterogeneous forest canopy: an appraisal of the effect of the precision level of crown description

Modeling of light transmission under heterogeneous forest canopy: an appraisal of the effect of the precision level of crown description

40 of 44 776 Table 3 Simulation results for the different mockups reconstruction. The p-value of the K-S test and the absolute discrepancy index (AD) were obtained with the measured light values as reference. Means with the same letter indicate that the difference between the means are not

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An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

Differences in forest structure within a given LC type (or PFT) can differ substantially (Kuuluvainen et al., 2012; New- ton, 1997), and preserving within-LC (or within-PFT) differ- ences in forest structure is necessary for more accurate mod- eling of surface fluxes in forests. While some land models assimilate local information on present day forest structure from satellite remote sensing to account for within-PFT vari- ation (e.g., Community Land Model 4.5, CLM4.5, Oleson et al., 2013; Jena Scheme of Atmosphere Biosphere Coupling in Hamburg, JSBACH, Reick, 2012), future structure must still be prescribed. Because land use transitions in model- ing simulation studies of anthropogenic LC change are often represented by a change in PFT area in land models, post- disturbance changes to structure within forest PFTs go unde- tected (Lawrence et al., 2012; Reick et al., 2013). Hence, a forest classification that accounts for major variation in key structural attributes, such as LAI or canopy height, may lead to better predictions of surface fluxes in forests, not only in studies of prescribed land cover and/or management change, but also for dynamic vegetation studies that rely on fixed PFT parameters obtained from lookup tables (LUTs). The time-invariant nature of the fixed parameter LUTs may be avoided by increasing the number of forest classes within a single forest PFT with sufficient differentiation in key struc- tural attributes (i.e., from young to mature forests). In addi- tion to grouping forests according to their shared phenologi- cal characteristics, further grouping according to their struc- tural characteristics (i.e., accounting for the effects of forest management) would strengthen prediction confidence in in- tensively managed regions.
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Monitoring changes of forest canopy density in a temperature forest using high-resolution aerial digital photography

Monitoring changes of forest canopy density in a temperature forest using high-resolution aerial digital photography

et al., 2012). Other automated methods using multispectral images have been developed using the indexes: Advanced vegetation ( AVI ), bare soil ( BI ), shadow ( SI ), and thermal ( TI ) obtained from Lansat-5 Thematic Mapper ( TM ) which have been employed to obtain an estimation of FCD in a de- ductive approach (Rikimaru, 1996). Some other methods include neural networks and fuzzy logic in this assessment, as a continuous variable from the seven bands of Landsat-7 Enhanced Thema- tic Mapper Plus ( ETM+ ) (Gopal and Woodcock, 1996; Boyd et al., 2002). Regression techniques have been used also to model the relationship between spectral response and canopy closure in conifer forest (Iverson et al., 1989). One example of comparison of methods predicted forest canopy density from a Landsat-7 ETM+ image by means of artificial neural networks, multiple linear re- gression, the forest canopy density mapper, and a maximum likelihood classification method; in this
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Transformasi Forest Canopy Density dan Second Modified Soil Adjusted Vegetation Index untuk Monitoring Degradasi Hutan Lindung dan Taman Nasional di Sarolangun Jambi

Transformasi Forest Canopy Density dan Second Modified Soil Adjusted Vegetation Index untuk Monitoring Degradasi Hutan Lindung dan Taman Nasional di Sarolangun Jambi

Shadow Index (SI) memiliki hubungan yang cukup erat dengan AVI. Hal ini disebabkan karena indeks bayangan tergantung dari ada tidaknya vegetasi pada hutan. Jika dalam hutan terdapat vegetasi maka dapat diketahui bahwa ada bayangan vegetasi yang menyertainya. Vegetasi yang dimaksud berupa pohon, namun apabila vegetasi berupa padang rumput maka tidak ada bayangan sehingga hubungan dari keduanya tidak selalu berbanding lurus. Kedua hubungan dari indeks ini tergantung dari keadaan hutan itu sendiri. Pada indeks SI semakin tinggi tingkat keabuan pada hutan menunjukkan bahwa indeks SI semakin tinggi dan berlaku pula sebaliknya semakin rendah tingkat keabuan/ cerah maka semakin rendah indeks SI. Warna hitam yang terdapat di beberapa lokasi tersebut merupakan gangguan yang berupa awan, sungai, dan daerah yang tidak bervegetasi.
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Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

Hemispherical photography Eucalypt A B S T R A C T The so-called clumping factor ( Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert e ffective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural con figurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55 –60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible.
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Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

Hemispherical photography Eucalypt A B S T R A C T The so-called clumping factor ( Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert e ffective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural con figurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55 –60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible.
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Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

There is a large body of literature focusing on the estimation of nitrogen in crops for monitoring and predicting crop yield [37,46,47,62]. However, to our knowledge, little research has been conducted for validating the use of vegetation indices for mixed forests (including both broadleaf and needle leaf species), whose structure and composition varies substantially from that of crops. Such mixed forest is common in temperate zone at mid-latitude. This study aimed to evaluate the performance of 32 vegetation indices derived from airborne hyperspectral imagery for estimating canopy foliar nitrogen in a mixed temperate forest. The commonly-used partial least squares regression was performed for comparison. These vegetation indices can be classified into three categories that are mostly correlated to biochemical and physical properties of vegetation (i.e., nitrogen, chlorophyll, and structure properties such as leaf area index (LAI)). The nitrogen indices are chosen based on the effect of the physical basis of nitrogen absorption features on canopy reflectance. The chlorophyll and structural indices were involved here to exploit their potential for estimating nitrogen through the biological links between nitrogen, chlorophyll, and canopy structure. 2. Materials and Methods
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Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Figure 7 is the column chart of NST values to vegetation indices mentioned above in four different cases for emergent vegetation. The high sensitivity parameters presented in the chart are the same as those in 4.1.1. The analysis results shown in Figure 7 are similar to those shown in Figure 4. In sparse cases, LAI is the most sensitive parameter to all vegetation indices, indicating that it is possible to retrieve LAI in sparse cases rather than dense cases via the vegetation index. Whereas the sensitivity of LAI in dense cases is weakened, just like that in Figure 4. The sensitivity of leaf parameters such as Cab and Car, and LIDFa rises strongly. In case (a) the sensitivity of LAI to NDAVI is greater than to the other indices, and the values of NST are 0.572, 0.805, 0.893 and 0.830 respectively. It can be attributed to the fact that the formulae of those indices all contain the near infrared (B8) band, which is the sensitive band of LAI in sparse cases according to Figure 4. However, NDAVI and WAVI, which are formulated by the blue band (B2), perform better than NDVI and SAVI formulated by the red band (B4) because LAI is more sensitive in the blue band than that in the red band. In fact, except for case (c), the NST of LAI to NDAVI is higher than to the other indices, which indicates that for emergent vegetation, NDAVI is more suitable for LAI inversion than the other indices except the deep-sparse case. This result is consistent with the conclusion of Villa et al. that for aquatic vegetation LAI is more sensitive to NDAVI than to the conventional terrestrial vegetation index [38]. In case (c), LAI scores higher sensitivity to NDVI, indicating that NDVI is a better option in deep-sparse case. The sensitivity of LAI to NDAVI in (c) is the lowest, probably due to the combined effect of increased water depth and water parameters in the blue band.
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Natural Canopy Damage and the Ecological Restoration of Fire-Indicative Groundcover Vegetation in an Oak-Pine Forest

Natural Canopy Damage and the Ecological Restoration of Fire-Indicative Groundcover Vegetation in an Oak-Pine Forest

these species following modern fire exclusion (as suggested by Nowacki and Abrams 2008) was unlikely, given the short time scale of modern fire exclusion relative to the low rates of dispersal and colonization of fire intolerant species (Matlack 1994). He therefore con- cluded that fire was not a historically import- ant factor in mesic deciduous forests. It is im- portant to recognize that Matlack and Nowacki and Abrams are not referring to the same “fire-intolerant” species. Matlack is primarily considering poorly dispersed forest herbs (Matlack 1994), whereas Nowacki and Abrams (2008) are primarily considering widely dis- persed tree species, such as red maple. In north Mississippi, invasion of upland oak for- ests from adjacent floodplains, mesic terraces, and steep ravines by red maple and fire-sensi- tive pioneer tree species such as sweetgum and winged elm (Ulmus alata Michx.) following modern fire exclusion is entirely plausible (Brewer 2001). Furthermore, to call red ma- ple, sweetgum, and winged elm fire-intolerant species is not entirely accurate. The ability of these fire-sensitive tree species to resprout fol- lowing fire could have prevented their com- plete elimination in the face of frequent fires, historically. Their slow regrowth following topkill by fire compared to oaks (Brose et al. 1999, Hutchinson et al. 2012, Cannon and Brewer 2013), however, likely prevented their escaping a “fire trap” (sensu Bond and Midg- ley 2001), relegating them to a sprout bank. Their persistence as sprouts prior to modern fire exclusion could partly explain their rarity among witness trees in fire prone areas during General Land Office surveys but also could have contributed to their subsequent rapid in- crease following fire exclusion. The resulting increase in canopy closure reduced the abun- dance of herbaceous species indicative of open woodlands and some light-flexible forest herbs, both of which (as shown in the current study) are tolerant of repeated fires.
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Estimation of wheat crop evapotranspiration using NDVI vegetation index

Estimation of wheat crop evapotranspiration using NDVI vegetation index

crop evapotranspiration through remote sensing and GIS techniques for Tarafeni South Main Canal (TSMC) Command area. The study area is located between 86°45’E to 87°15’E longitude and 22°15’N to 22°45’N latitude is a part of right bank main canal of Kangsabati project and supplemented with a barrage on Tarafeni river in the Pashchim Midnapur districts of West Bengal state of India. The gross irrigation com- mand area of TSMC is about 70,450 ha and major crops grown are paddy in monsoon season, wheat, po- tato, mustard and vegetables in winter season (Gontia and Tiwari, 2004). At present overall irrigation effi- ciency (the ratio of water available for crop to the wa- ter supplied from reservoir) for paddy crop is 37% and about 45% for other crops as per the annual report- 2001 of Kangsabati Command Area Development Authority (Gontia and Tiwari, 2010). Low irrigation efficiency can be improved by proper irrigation scheduling as per crop water demand. About 50% of irrigation command area is under cultivation, more than 40% area is covered by forest, and remaining area is orchards, fallow land, wasteland and establishments. Soil of the location is acid lateritic with sandy loam in texture. The climate of TSMC Command area is classified as humid and subtropical. It is characterized as hot and humid in the summer (April and May), rainy
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Measuring Forest Canopy Height Using a Combination of LIDAR and Aerial Photography Data

Measuring Forest Canopy Height Using a Combination of LIDAR and Aerial Photography Data

It has been demonstrated that the height of forest canopies can be measured with a good accuracy using small footprint lidars. This is essentially accomplished by subtracting the last return altitude (ground) from the corresponding first return altitude (canopy surface). The technique is considered superior to photogrammetric methods mainly because the ground level, which is difficult to see on aerial photos of densely forested areas, can be well identified using small footprint lidars. However, lidar cannot be used to characterized past forest states, while these can be assessed, and photogrammetically measured, in the wealth of historical aerial photographs most developed countries possess. Our goal is to replace the first return lidar data by altitude models derived from aerial photos in order to map forest canopy height changes of the past decades. This paper presents the first methodological steps which consist in comparing canopy heights obtained from lidar data only to a combination of lidar and photogrammetry data. The lidar data was acquired over an area of the boreal forest in Quebec, Canada, in 1998, using Optech’s ALTM1020 flying at an altitude of 700 m. Two stereo-pairs of aerial black and white photographs were used: 1) a pair of 1:15,000 photos taken in 1994, and 2) a pair of 1:40,000 photos taken in 1998. A lidar canopy height model (CHM) was created by subtracting ground altitudes from canopy altitudes. Aerial photo altitude models were derived using the image correlation methods of Virtuozo 3.2 software. The ground level altitudinal fit between the aerial photo altitude model and the lidar data was checked on rock outcrops. A photo CHM was created by subtracting the lidar ground altitude model from the aerial photo altitude model. The photo CHM and the lidar CHM show a good degree of correlation.
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Vegetation Assessment of Peat Swamp Forest Using Remote Sensing

Vegetation Assessment of Peat Swamp Forest Using Remote Sensing

According to vegetation index, in order to detect level of greenness of remnant peat swamp forest due to the harvesting system and land clearing activities, two sites-East and West as illustrated by black boxes in Fig. 1 were used for the Vegetation Index analysis. Lillesand and Kiefer [4] noted that the use of Normalized Difference Vegetation Index (NDVI) has been much favored for studying many vegetation phenomena. Bands 3 (Red) and 4 (Near Infra-red) were used to compute the NDVI. The NDVI results generated were reclassified into the four level greenness categorized into non-green, low green, medium green and high green. The Vegetation Index scale ranges from-1 through 0 to +1 with negative values to zero value represent no vegetation.
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Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Weed management is a major component of a soybean ( Glycine max L.) production system; thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties of plants have shown promise as tools to enhance differences among plants. The objective of this study was to evaluate normalized difference vegetation indices derived from multispectral leaf reflectance data as input into random forest machine learner to differentiate soybean and three broad leaf weeds: Palmer amaranth ( Amaranthus palmeri L.), redroot pig- weed ( A. retroflexus L.), and velvetleaf ( Abutilon theophrasti Medik). Leaf reflec- tance measurements were acquired from plants grown in two separate greenhouse experiments conducted in 2014. Twelve normalized difference vegetation indices were derived from the reflectance measurements, including advanced, green, green- red, green-blue, and normalized difference vegetation indices, shortwave infrared water stress indices, normalized difference pigment and red edge indices, and struc- ture insensitive pigment index. Using the twelve vegetation indices as input variables, the conditional inference version of random forest (cforest) readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pig- weed) and from each other with classification accuracies ranging from 93.3% to 100%. The greatest errors were observed between the two pigweed classes, with clas- sification accuracies ranging from 70% to 93.3%. Results suggest combining them into one class to increase classification accuracy. Vegetation indices results were equivalent to or slightly better than results obtained with sixteen multispectral bands used as input data into cforest. This research further supports using vegetation in- dices and machine learning algorithms such as cforest as decision support tools for weed identification.
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Herbicides Forest Vegetation (PSU)

Herbicides Forest Vegetation (PSU)

Herbicides are a proven safe and effec- tive method for managing forest vegeta- tion and are appropriate for achieving many objectives, including regeneration establishment, increased timber produc- tion, enhanced wildlife habitat, non- native plant control, and road and facility maintenance. When properly applied, herbicides can increase property value, productivity, aesthetics, and util- ity. However, understand that choices exist. A well-developed and imple- mented integrated pest management plan will include alternative vegetation control approaches with and without the use of herbicides. This publication will help you identify the most efficient, environmentally sound, and cost- effective solution for addressing your forest vegetation management needs.
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Small Scale Spatio Temporal Variabilities in Soil Nitrogen, Leaf Nitrogen, and Canopy Normalized Difference Vegetation Index of Cotton

Small Scale Spatio Temporal Variabilities in Soil Nitrogen, Leaf Nitrogen, and Canopy Normalized Difference Vegetation Index of Cotton

Canopy NDVI is a good vegetation index that has been increasingly used to estimate plant N nutrition status for guiding variable-rate precision N applications. The Arc View maps of NDVI were somewhat similar at the early, mid, and late bloom stages in 2009 (Figure 6), which showed a similar trend as leaf N did. The NDVI values were generally lower at the north-west corner than the rest of the field. The highest NDVI value was 0.853, 0.865, and 0.878, while the lowest NDVI reading was only 0.693, 0.677, and 0.686 out of the test at the early, mid, and late bloom stages, respectively. The dif- ference between the highest and lowest NDVI values was 0.160, 0.188, and 1.92 for the three growth stages, respectively. Overall, the pattern and degree of the spatial variabil- ity in NDVI did not change much during the 2009 growing season.
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