Vegetation Condition Monitoring Research Articles

Extraction and monitoring of vegetation coverage based on uncrewed aerial vehicle visible image in a post gold mining area

Vegetation coverage reflects the degree of environmental degradation. Timely and effective monitoring of vegetation conditions is the basis for promoting vegetation protection and improving the ecological environment of mining areas. Exploring vegetation coverage extraction methods and selecting the optimal vegetation index in mining areas can provide scientific reference for estimating vegetation coverage based on vegetation index in mining areas. Uncrewed aerial vehicles (UAVs) are widely used because of their fast real-time performance, high spatial resolution, and easy accessibility. In this study, the performances of nine visible vegetation indices and two threshold segmentation methods for extracting vegetation coverage in a post-gold mining area in the Qinling Mountains were comprehensively compared using visible spectrum UAV images. Of the nine indices, the excess green index (EXG) and visible-band difference vegetation index (VDVI) were the most effective in discriminating between vegetation and non-vegetation by visual interpretation. In addition, the accuracy of the bimodal histogram threshold method in extracting vegetation coverage was higher than that of Otsu’s threshold method. The bimodal histogram threshold method combined with EXG yielded optimal extraction results. Based on optimal methods, the total percentages of fractional vegetation coverage in 2019, 2020, and 2021 were 31.47%, 34.08%, and 42.77%, respectively, indicating that the vegetation in the mining area improved. These results provide valuable guidance for extracting vegetation information and evaluating vegetation restoration in mining areas.

Monitoring of vegetation conditions in the Kedrovsky coal mine area based on satellite data

Monitoring of vegetation conditions in the Kedrovsky coal mine area based on satellite data

Silage maize yield estimation by using planetscope, sentinel-2A and landsat 8 OLI satellite images

The early prediction of crop yield is a vital component of agricultural planning and policy decision-making. In order to achieve this, many countries utilize conventional techniques such as crop growth models that simulate agricultural applications. Alternatively, some approaches involve the spatio-temporal monitoring of vegetation conditions. In this study, we aimed to evaluate the potential for silage maize yield estimation using vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Soil-Adjustment Vegetation Index (SAVI), and Simple Ratio (SR), through the use of PlanetScope, Sentinel-2A, and Landsat 8 OLI satellite images. Linear regression was employed to examine the relationship between silage maize yield and calculated spectral vegetation indices (SVI) at various dates. The results revealed significant correlations between remotely sensed SVI and silage maize yield values for four large-scale plots. Specifically, it was found that silage maize yield could be estimated most accurately using SR between Days After Sowing (DAS) 73 and DAS 76 at a p-value of <0.01. Additionally, yield could be successfully estimated using these three satellite images and indices between DAS 33 and DAS 69 at the p<0.05 level.

Resource Communication: ForestAz - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal)

Aim of study: ForestAz application was developed to (i) map Azorean forest areas accurately through semiautomatic supervised classification; (ii) assess vegetation condition (e.g., greenness and moisture) by computing and comparing several spectral indices; and (iii) quantitatively evaluate the stocks and dynamics of aboveground carbon (AGC) sequestrated by Azorean forest areas.&#x0D; Area of study: ForestAz focuses primarily on the Public Forest Perimeter of S. Miguel Island (Archipelago of the Azores, Portugal), with about 3808 hectares.&#x0D; Material and methods: ForestAz was developed with Javascript for the Google Earth Engine platform, relying solely on open satellite remote sensing data, as Copernicus Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (multispectral).&#x0D; Main results: By accurately mapping S. Miguel island forest areas using a detailed species-based vegetation mapping approach; by allowing frequent and periodic monitoring of vegetation condition; and by quantitatively assessing the stocks and dynamics of AGC by these forest areas, this remote sensing-based application may constitute a robust and low-cost operational tool able to support local/regional decision-making on forest planning and management.&#x0D; Research highlights: This collaborative initiative between the University of the Azores and the Azores Regional Authority in Forest Affairs was selected to be one of the 99 user stories by local and regional authorities described in the catalog edited by the European Commission, the Network of European Regions Using Space Technologies (NEREUS Association), and the European Space Agency (ESA).

A Framework to Assess the Potential Uncertainties of Three FPAR Products

AbstractThe remote sensing products of the fraction of photosynthetically active radiation (FPAR) are widely used in large‐scale terrestrial vegetation condition monitoring and ecological modeling. However, the validation and evaluation of these products are still insufficient due to limited ground measurements. A framework is proposed in this study that does not rely on ground FPAR observations to evaluate the potential uncertainties of three FPAR data sets over a large scale. Two independent methods, including a statistical method named extended triple colocation (ETC) and FPAR‐based gross primary production (GPP) comparative evaluation at the site level, are integrated. The framework was applied to three widely used FPAR products, including GLASS (Global Land Surface Satellite), GEOV2 (version 2 of the COPERNICUS 1‐km product), and MODIS (Moderate Resolution Imaging Spectroradiometer), in China from 2001–2015. The root‐mean‐square error (RMSE) and correlation coefficient (R) based on the ETC method showed that at the national spatial scale, GEOV2‐FPAR had the highest precision (RMSEFPAR = 0.0367, RFPAR = 0.9012), followed by MODIS‐FPAR (RMSEFPAR = 0.0482, RFPAR = 0.8392) and GLASS‐FPAR (RMSEFPAR = 0.0536, RFPAR = 0.736). In particular, each FPAR data set was locally optimal over some regions. Using a light use efficiency model, GPP was calculated using these three FPAR data sets and other identical variables and parameters at 22 eddy flux sites. General coherent results were found in both GPP evaluation and site‐level ETC analysis of these three FPAR data sets. Therefore, we conclude that this framework is reliable and applicable in evaluating the potential uncertainties of FPAR.

Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation

Drought disaster is one of the major factors restricting the development of vegetation across a wide variety of environments. Monitoring the temporal and spatial dynamics of drought episodes in the study area is crucial for environmental and ecosystem conservation. This study assesses drought disaster by utilising space-based data and R programming for drought years 2003, 2007, 2012 and 2019 in the Free State Province, South Africa. Results revealed that the study area witnessed drought events in the year 2003 where March, August, September, October, November and December were more affected by drought disaster events. It was further observed that February and March were affected by extreme drought conditions in the year 2007. In year 2012, January, October, November and December, there exist moderate to severe drought conditions in the study area where some regions were more affected than the other. Finally, year 2019 witnessed variations in drought event distributions across the months with January, October and November witnessing severe to extreme drought conditions from about 0 to 30% drought values. Overall, this study shows that the 16-day Terra-MODIS composite and EVI products are sensitive to stressors associated with drought. The Vegetation Condition Monitoring Index (VCI) based on MODIS is suited for monitoring drought disasters. The technique used in this study revealed the suitability of MODIS data for assessing drought conditions and their potential environmental impacts.

Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub &lt;em&gt;Salix polaris&lt;/em&gt;, the herb &lt;em&gt;Bistorta vivipara&lt;/em&gt;, and the prostrate semievergreen shrub &lt;em&gt;Dryas octopetala&lt;/em&gt;. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that &lt;em&gt;S. polaris&lt;/em&gt; and &lt;em&gt;B. vivipara&lt;/em&gt; were in good health, while the health status of &lt;em&gt;D. octopetala&lt;/em&gt; was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas.

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review

Native vegetation around the world is under threat from historical and ongoing clearance, overgrazing, invasive species, increasing soil and water salinity, altered fire regimes, poor land management and other factors, resulting in a degradation of natural ecosystem services. Consequently, maintaining and improving native vegetation condition is a target frequently adopted by natural resource managers and government agencies world-wide. Adequate monitoring of vegetation condition remains a prerequisite for environmental decision-making and for tracking progress towards management goals. Throughout we consider vegetation condition to include the compositional, structural and functional attributes of vegetation relative to undisturbed vegetation of the same type.Site-based methods have long been used to assess compositional, structural and functional attributes as indicators of vegetation condition, and these methods continue to be used widely today. With developing technologies, remote sensing methods are being employed increasingly for monitoring a range of remotely detectable properties of vegetation, and there is now a growing demand to explicitly integrate the two approaches for mapping and monitoring vegetation condition across a range of scales.Here we review the attributes of vegetation identified as important for monitoring vegetation condition, those indicators that are best measured using traditional site-based methods and those that are more readily detectable using remote sensing methods, including their application in operational programmes within Australia. Further to this we review recent literature on the integration of the two approaches for monitoring indicators of vegetation condition.We find that remote sensing methods have the advantage of offering broad scale automated and repeatable methods for monitoring indicators of vegetation condition, but when combined with detailed ecological site-based data, together can improve monitoring for answering ecological questions across a range of scales. Further work, however, is required to effectively integrate the two approaches for mapping and monitoring vegetation condition.

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.

Application of remote sensing techniques toward the role of traditional water bodies with respect to vegetation conditions

Sri Lanka being an agrarian country, the role of water is important for agricultural production. In Sri Lanka, various tank cascade systems, earthen dams and distribution canals have been accepted as few of the most complex ancient traditional water systems of the world. Rainfall, surface water, groundwater and runoff are linked with each other, they have close interactions to land cover classes such as forests and agriculture. The monitoring of vegetation conditions can show subsurface manifestations of groundwater. In this study, an effort to understand the role of traditional water reservoirs and groundwater recharge was made using remote sensing techniques. We have analyzed various vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI-2), Soil-Adjusted Vegetation Index (SAVI), tasselled cap transformation analysis (TCA brightness, greenness and wetness) and their relations with the existence of soil, vegetation and water. Result shows that EVI, SAVI, and TCA-based Greenness Index indicates good relationship with the vegetation conditions as compared to other indices. Therefore, these indices could play a crucial role in depicting the interaction between soil, vegetation, and water. However, multi-temporal observations can provide significant results about these interactions more accurately.

Investigation of aggregation effects in vegetation condition monitoring at a national scale

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. Vegetation indices (VIs), mathematical transformations of reflectance bands, have played an important role in vegetation monitoring, as they depict the abundance and health of vegetation. Instead of storing raster VI maps, aggregated statistics can be derived and used in long-term monitoring. The aggregation schemes (zonations) used in Greece are the forest service units, the fire service units, and the administrative units. The purpose of this work was to explore the effect of the modifiable areal unit problem (MAUP) in vegetation condition monitoring at the above-mentioned aggregation schemes using 16 days Normalized Difference Vegetation Index (NDVI) composites acquired by the moderate resolution imaging spectroradiometer satellite sensor. The effects of aggregation in the context of MAUP were examined by analyzing variance, from which the among polygon variation (objects' heterogeneity) and the within polygon variation (pixels' homogeneity) were derived. Significant differences in objects' heterogeneity were observed when aggregating at the three aggregation schemes; therefore there is a MAUP effect in monitoring vegetation condition on a nationwide scale in Greece with NDVI. Monitoring using the fire service units has significantly higher pixels' homogeneity; therefore there is indication that it is the most appropriate for monitoring vegetation condition on a nationwide scale in Greece with NDVI. Results were consistent between the two major types of vegetation, natural and agricultural. According to the statistical validation, conclusions based on the examined years (2003 and 2004) are justified.

Vegetation recovery monitoring over the waste dump in Haizhou opencast coalmine area, China, during 1975–2000 using NDVI and VF index

Vegetation condition monitoring has been done from 1975 to 2000 in the waste dump of Haizhou opencast coalmine area, China, using remote sensing techniques with the objective of improving our understanding of the temporal and spatial variation of vegetation recovery in the mining dump. Four historical vegetation indexes (NDVI, VF, soil brightness and vegetation greenness) from two Landsat 2 MSS images and two Landsat 5 TM images are extracted and analyzed. For the purpose of comparison and analysis two improved techniques such as normalization grading of change slope and image segmentation were used in this study. Based on the results obtained through the above analysis two conclusions are derived: (1) vegetation recovery in the study area is in an improved condition, (2) two remote sensing based vegetation indexes such as VF and NDVI are the optimal parameters to monitor vegetation condition, which could be used as the indicators of land reclamation progress in the mining area.

Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral-resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content. For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters providing optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.

Assessment and monitoring of vegetation condition: Moving forward

Assessment and monitoring of vegetation condition: Moving forward

Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO: The ARTEMIS and DIANA systems

Since August 1988, after a development period of 3 years, the FAO Remote Sensing Centre has been operating the Africa Real Time Environmental Monitoring Information System (ARTEMIS) in support of the Global Information and Early Warning System on Food and Agriculture and the Desert Locust Plague Prevention Programme of FAO. The ARTEMIS system was implemented by FAO in close co-operation with NASA Goddard Space Flight Center, U.S.A.; the National Aerospace Laboratory of The Netherlands and the University of Reading, U.K. ARTEMIS is a highly automated data acquisition, pre- and thematic processing, production and archiving system for real-time precipitation assessment and near real-time vegetation condition monitoring of Africa, the Near East and Southwest Asia, based on hourly Meteosat thermal infrared and NOAA AVHRR data. The vegetation condition assessment capability is currently being expanded to include the rest of Asia and Latin America. ARTEMIS data products, generated by the system on a 10-day and monthly basis, are currently used operationally by a variety of users at FAO Headquarters and by regional and national food security early warning systems in sixteen Eastern and Southern African countries. The ARTEMIS system plays an important role in the generation and archiving of global satellite derived environmental data sets for use by FAO and other organizations with global monitoring and assessment mandates. An extensive ARTEMIS rainfall estimation calibration programme, covering Sahelian countries as well as Eastern and Southern Africa, has been developed in co-operation with the University of Reading, U.K. and regional FAO field projects. This programme was started in 1990 and will continue through 1995 to obtain statistically valid regression parameters for homogeneous climatic zones to allow automated translation of satellite derived cold cloud duration data into quantities of estimated rainfall. Jointly with the European Space Agency, FAO Has been implementing during 1989–1992 a dedicated satellite communications system, DIANA (Direct Information Access Network for Africa), which allows real-time transmission of high volume ARTEMIS digital products to user terminals in Nairobi, Kenya: Accra, Ghana and Harare, Zimbabwe. The DIANA system, which operates through the Intelsat satellite over the Indian Ocean and the italian Intelsat groundstation of Telespazio in Fucino, is currently being tested and demonstrated for a wide variety of applications of an operational, technical and administrative nature.