scholarly journals USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 711-718 ◽  
Author(s):  
R. Minařík ◽  
J. Langhammer
Keyword(s):  
Bark Beetle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Forest Disturbance ◽  
Methodological Approach ◽  
Detection And Tracking ◽  
Red Edge ◽  
Tree Stress ◽  
Simple Ratio

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.

scholarly journals USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 711-718 ◽  
Author(s):  
R. Minařík ◽  
J. Langhammer
Keyword(s):  
Bark Beetle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Forest Disturbance ◽  
Methodological Approach ◽  
Detection And Tracking ◽  
Red Edge ◽  
Tree Stress ◽  
Simple Ratio

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.

scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

scholarly journals Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3965 ◽  
Author(s):  
Liang Zhao ◽  
Zhigang Liu ◽  
Shan Xu ◽  
Xue He ◽  
Zhuoya Ni ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Quadratic Function ◽  
High Temporal Resolution ◽  
Area Index ◽  
Red Edge ◽  
Jointing Stage ◽  
Simple Ratio

The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).

scholarly journals Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Daniel O. Wasonga ◽  
Afrane Yaw ◽  
Jouko Kleemola ◽  
Laura Alakukku ◽  
Pirjo S.A. Mäkelä
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
High Energy ◽  
Plant Performance ◽  
Estimation Accuracy ◽  
Management Decisions ◽  
Lower Estimation ◽  
Low K

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.

scholarly journals Application of Phenotyping Methods in Detection of Drought and Salinity Stress in Basil (Ocimum basilicum L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Boris Lazarević ◽  
Zlatko Šatović ◽  
Ana Nimac ◽  
Monika Vidak ◽  
Jerko Gunjača ◽  
...  
Keyword(s):  
Drought Stress ◽  
Salinity Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Ocimum Basilicum ◽  
Phenotypic Traits ◽  
Severe Drought ◽  
Moderate Salinity ◽  
Automated Phenotyping

Basil is one of the most widespread aromatic and medicinal plants, which is often grown in drought- and salinity-prone regions. Often co-occurrence of drought and salinity stresses in agroecosystems and similarities of symptoms which they cause on plants complicates the differentiation among them. Development of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis. In this study, we have used different phenotyping techniques including chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning, aiming to quantify changes in basil phenotypic traits under early and prolonged drought and salinity stress and to determine traits which could differentiate among drought and salinity stressed basil plants. Ocimum basilicum “Genovese” was grown in a growth chamber under well-watered control [45–50% volumetric water content (VWC)], moderate salinity stress (100 mM NaCl), severe salinity stress (200 mM NaCl), moderate drought stress (25–30% VWC), and severe drought stress (15–20% VWC). Phenotypic traits were measured for 3 weeks in 7-day intervals. Automated phenotyping techniques were able to detect basil responses to early and prolonged salinity and drought stress. In addition, several phenotypic traits were able to differentiate among salinity and drought. At early stages, low anthocyanin index (ARI), chlorophyll index (CHI), and hue (HUE2D), and higher reflectance in red (RRed), reflectance in green (RGreen), and leaf inclination (LINC) indicated drought stress. At later stress stages, maximum fluorescence (Fm), HUE2D, normalized difference vegetation index (NDVI), and LINC contribute the most to the differentiation among drought and non-stressed as well as among drought and salinity stressed plants. ARI and electron transport rate (ETR) were best for differentiation of salinity stressed plants from non-stressed plants both at early and prolonged stress.

scholarly journals Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

2019 ◽  
Vol 11 (18) ◽  
pp. 2127
Author(s):  
Polinova ◽  
Salinas ◽  
Bonfante ◽  
Brook
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Future Climate Change ◽  
Weather Forecasts ◽  
Red Edge ◽  
Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

scholarly journals Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms

2019 ◽  
Vol 11 (17) ◽  
pp. 2019 ◽  
Author(s):  
Sergio Fagherazzi ◽  
Giovanna Nordio ◽  
Keila Munz ◽  
Daniele Catucci ◽  
William S. Kearney
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Remote Sensing Data ◽  
Storm Events ◽  
Coastal Forests ◽  
The North ◽  
Significant Difference

Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance.

scholarly journals Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

2019 ◽  
Vol 11 (23) ◽  
pp. 2807 ◽  
Author(s):  
Arthur Bayle ◽  
Bradley Carlson ◽  
Vincent Thierion ◽  
Marc Isenmann ◽  
Philippe Choler
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Model Performance ◽  
Mountain Range ◽  
Rhododendron Ferrugineum ◽  
Red Edge ◽  
Rocky Habitats ◽  
Edge Band ◽  
Sentinel 2

Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.

scholarly journals ОЦЕНКА УРОВНЯ ОЗЕЛЕНЕНИЯ ГОРОДА БИРОБИДЖАНА С ПРИМЕНЕНИЕМ МУЛЬТИСПЕКТРАЛЬНЫХ ДАННЫХ

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Д.М. Фетисов ◽  
Д.В. Жучков ◽  
М.В. Горюхин
Keyword(s):  
Green Infrastructure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Natural Vegetation ◽  
Medium Size ◽  
Functional Zones ◽  
Functional Areas ◽  
The City ◽  
And Storage

The urban greenness distribution between functional areas of a medium-size city Birobidzhan was assessed. To this end, normalized difference vegetation index (NDVI) values were calculated based on Sentinel 2 multispectral imaging. Birobidzhan is characterized by a large scatter of NDVI values (from –0.5 to +1). Areas with high levels of greenery are prevalent. They are found in different types of functional zones, but are specific mainly to natural recreational, agricultural, and individual build-up zones as well as to special areas. The spatial distribution of green infrastructure is highly contrast. The downtown part as well as the industrial and storage zones feature a combination of built-up areas with dense woody vegetation, which is often represented by fragments of preserved natural vegetation. In addition, a feature of the contrast is that low level of tree greenness is characteristic for the built-up districts of the city. Thus, in the city of Birobidzhan, ecological functions are largely performed by the natural vegetation present in the natural recreational zones on 70% of the city's area.

Sign in / Sign up

Export Citation Format

Share Document