National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.

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Status of Phenological Research Using Sentinel-2 Data: A Review

Remote Sensing ◽

10.3390/rs12172760 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2760

Author(s):

Gourav Misra ◽

Fiona Cawkwell ◽

Astrid Wingler

Keyword(s):

Vegetation Index ◽

Temporal Frequency ◽

Short Wave ◽

Current State ◽

Red Edge ◽

Natural Grasslands ◽

Wide Range ◽

Normalised Difference Vegetation Index ◽

Satellite Sensors ◽

Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

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Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100405 ◽

2018 ◽

Vol 7 (10) ◽

pp. 405 ◽

Cited By ~ 12

Author(s):

Urška Kanjir ◽

Nataša Đurić ◽

Tatjana Veljanovski

Keyword(s):

Land Use ◽

Time Series ◽

Agricultural Policy ◽

Common Agricultural Policy ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Agricultural Land Use ◽

Land Use Control ◽

Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

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Incorporating vegetation trends to the MEDALUS-ESA approach for assessing environmental sensitivity at the national scale: the case of Kenya

10.5194/egusphere-egu21-16468 ◽

2021 ◽

Author(s):

Elias Symeonakis ◽

Eva Arnau-Rosalén ◽

Antony Wandera ◽

Thomas Higginbottom ◽

Bradley Cain

Keyword(s):

National Parks ◽

Land Degradation ◽

Vegetation Index ◽

Sub Saharan Africa ◽

Mitigation Measures ◽

Environmental Sensitivity ◽

National Scale ◽

Livestock Density ◽

Normalised Difference Vegetation Index ◽

Sub Saharan

Land degradation is one of the main causes of loss of productivity and ecosystem services worldwide. According to the United Nations Convention to Combat Desertification (UNCCD), sub-Saharan Africa is on a path to experiencing some of the strongest increases in pressures on land and land-based resources than any other continent. Assessing the sensitivity of sub-Saharan African countries to land degradation is, therefore, important for identifying areas of concern, setting a baseline for national land degradation neutrality targets, and for the prioritisation of mitigation measures. The widely used MEDALUS-ESA framework is employed here to assess the sensitivity of Kenya to land degradation using the year 2010 as a baseline. We modify the MEDALUS-ESA approach by adding two important variables that are closely linked with observed land degradation in Kenya: soil erosion and livestock density. Altogether, 16 indicators are estimated from existing global-to-national-scale land cover, vegetation (MCD12Q1, MOD44B), soil (ISRIC African SoilGrids), elevation (SRTM), population and livestock density data, divided into 4 main environmental quality indices (vegetation, soil, climate and management). In order to address the dynamic nature of the land degradation process, we incorporate two additional vegetation indicators: the statistically significant (p&#8804; 0.05) trend over the last three decades in the Normalised Difference Vegetation Index (NDVI) and the Rain Use Efficiency (RUE; estimated using the GIMMS3g dense NDVI dense time-series and precipitation from CHIRPS). Our results show that ~40% of the country is in critical and ~48% in fragile condition, with respect to environmental sensitivity. Our approach is successful in identifying areas of known long-term degradation, for example the rangelands South and East of Nairobi (e.g. Machacos and Kitengela) and the parts of the northern rangelands (e.g. Yamicha and eastern parts of Isiolo District). It is also successful in mapping the areas of least concern, including some of the major protected areas(e.g. Tsavo National Parks, Meru National Park and the Masai Mara National Reserve) and forested areas (Mt Kenya and the Aberdares). Our modification of the MEDALUS-ESA is an important tool that can be employed at the national scale using free and open-access data to assess environmental sensitivity and assist in the UNCCD efforts to successfully define land degradation neutrality targets.

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Corrigendum to: Influence of climatic factors on variation in the Normalised Difference Vegetation Index in Mongolian Plateau grasslands

The Rangeland Journal ◽

10.1071/rj16073_co ◽

2018 ◽

Vol 40 (2) ◽

pp. 205

Author(s):

Xu-Juan Cao ◽

Qing-Zhu Gao ◽

Ganjurjav Hasbagan ◽

Yan Liang ◽

Wen-Han Li ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Vegetation Index ◽

Climatic Factors ◽

Growing Season ◽

Limiting Factor ◽

Spatial And Temporal Variability ◽

Climate Factors ◽

Mongolian Plateau ◽

Normalised Difference Vegetation Index

Climate change will affect how the Normalised Difference Vegetation Index (NDVI), which is correlated with climate factors, varies in space and over time. The Mongolian Plateau is an arid and semi-arid area, 64% covered by grassland, which is extremely sensitive to climate change. Its climate has shown a warming and drying trend at both annual and seasonal scales. We analysed NDVI and climate variation characteristics and the relationships between them for Mongolian Plateau grasslands from 1981 to 2013. The results showed spatial and temporal differences in the variation of NDVI. Precipitation showed the strongest correlation with NDVI (43% of plateau area correlated with total annual precipitation and 44% with total precipitation in the growing season, from May to September), followed by potential evapotranspiration (27% annual, and 30% growing season), temperature (7% annual, 16% growing season) and cloud cover (10% annual, 12% growing season). These findings confirm that moisture is the most important limiting factor for grassland vegetation growth on the Mongolian Plateau. Changes in land use help to explain variations in NDVI in 40% of the plateau, where no correlation with climate factors was found. Our results indicate that vegetation primary productivity will decrease if warming and drying trends continue but decreases will be less substantial if further warming, predicted as highly likely, is not accompanied by further drying, for which predictions are less certain. Continuing spatial and temporal variability can be expected, including as a result of land use changes.

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Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

Italian Journal of Agronomy ◽

10.4081/ija.2017.897 ◽

2017 ◽

Vol 12 (3) ◽

Cited By ~ 3

Author(s):

Rafia Mumtaz ◽

Shahbaz Baig ◽

Iram Fatima

Keyword(s):

Climate Change ◽

Land Use ◽

Crop Yield ◽

Vegetation Index ◽

Wheat Yield ◽

Least Square ◽

Wheat Crop ◽

Main Challenge ◽

Normalised Difference Vegetation Index ◽

The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (R2) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

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Appraisal of Deforestation in District Mansehra through Sentinel-2 and Landsat Imagery.

International Journal of Agriculture & Sustainable Development ◽

10.33411/ijasd/20190102 ◽

2019 ◽

Vol 01 (01) ◽

Cited By ~ 2

Author(s):

Ghulam Nabi ◽

Imran Siddique Kaukab ◽

Syed Shah Zain Abbas ◽

Muhammad Saifullah ◽

Maira Malik ◽

...

Keyword(s):

Land Use ◽

Vegetation Index ◽

Landsat Imagery ◽

Temporal Variations ◽

Kappa Coefficient ◽

Khyber Pakhtunkhwa ◽

Healthy Environment ◽

Time Periods ◽

Spectral Responses ◽

Sentinel 2

Forests are the main source of food/wood and are important for a healthy environment. Removal of trees from forested landcover is known as deforestation. The main objective of this study was to estimate temporal variations in forested landcover located in district Mansehra for the years from 2008 to 2018 with two comparative time periods 1) 2008 to 2013 and 2) 2013 to 2018. Results indicates about deforestation in the study area during 2008-2013 and afforestation in 2013-2018. Vegetative landcover was increased from 43.3% to 47.2%. Afforestation at tehsil level showed that the vegetative area in Balakot was increased from 26.6% to 29.8%. Similarly, vegetation index increased from 72.2% to 74.42% in Manshera and 82.7% to 83.5% in Oghi. Kappa coefficient performed well to access accuracy of classified imagery which was maximum for the classified map obtained using Sentinel-2 dataset, therefore, Sentinel-2 imagery was proved more reliable in comparison to Landsat imagery. The spectral responses of various land use classes were also mapped which are useful of other researches to recognize features through optical datasets. Results proved the sincere efforts of Khyber Pakhtunkhwa government in promotion of vegetated landcover. The coverage of KPK project must be enhanced for increasing vegetation for a green Pakistan.

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Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

Journal of Forest Science ◽

10.17221/28/2020-jfs ◽

2020 ◽

Vol 66 (No. 8) ◽

pp. 339-348

Author(s):

Viktoriia Lovynska ◽

Yuriy Buchavyi ◽

Petro Lakyda ◽

Svitlana Sytnyk ◽

Yuriy Gritzan ◽

...

Keyword(s):

Aboveground Biomass ◽

Vegetation Index ◽

Spectral Characteristics ◽

Free Access ◽

Biophysical Parameters ◽

Normalised Difference Vegetation Index ◽

Pine Stands ◽

Predicted Values ◽

Vegetative Indices ◽

Sentinel 2

The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetation index (NDVI) and transformed vegetation index (TVI) vegetative indices and the fraction of absorbed photosynthetic active radiation (FARAP) and fraction of vegetation cover (FCOVER) biophysical parameters. To generate the models of determining the forest aboveground biomass (AGB), we used both the single- and two-factor models, the most optimum of which were those containing the NDVI predictor separately and in combination with the FCOVER predictor. The predicted values of the total AGB for the mentioned models equalled 32.5 to 236.3 and 39.9 to 253.4 t·ha–1. We performed mapping of the AGB of pine stands of the Northern Steppe using multi-spectral Sentinel-2 images, particularly the spectral characteristics of their derivatives (vegetative indices, biophysical parameters). This study demonstrated promising results for conducting an AGB-mapping of pine woods in the studied region using free-access resources.

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Outmigration Drives Cropland Decline and Woodland Increase in Rural Regions of Southwest China

Land ◽

10.3390/land9110443 ◽

2020 ◽

Vol 9 (11) ◽

pp. 443

Author(s):

Yi Yu ◽

Tingbao Xu ◽

Tao Wang

Keyword(s):

Land Use ◽

Vegetation Index ◽

Agricultural Sector ◽

Southwest China ◽

Labour Force ◽

Arable Land ◽

Sustainable Land Use ◽

Land Use Patterns ◽

Rural Regions ◽

Normalised Difference Vegetation Index

Rapid urbanisation in China has led to massive outmigration in rural regions, which has changed the regional labour force structure and can have various profound impacts as a result. This research used a case study in Southwest China to investigate how regional land use patterns have been changed in the context of rural outmigration and assessed the resulting dynamics on local ecological environment. The key findings include: (1) The local land conversion process was mainly characterised by the conversion of farmland (−18.3%) to residential area (+268.3%) and woodland (+55.6%) during 2000–2018; (2) about 83.7% of area showed a statistically significant increase in the normalised difference vegetation index (NDVI), which was not due to human interference factors (e.g., afforestation). Correlation analyses showed that depopulation (R = −0.514, p < 0.01) and local mild temperature (R = 0.505, p < 0.01) could be the main contributors. Only 2.5% of the area had decreased NDVI and this was directly caused by human activities (e.g., urban area expansion). These results implied that vegetation improvement can occur in the context of depopulation and farmland reduction, which did not significantly threaten the local agricultural sector. It then could be a good choice to allow those high-slope and biophysically poor farmlands to undergo forest succession rather than cultivation. Farmers in Southwest China should make a full use of the existing low-slope arable land to curb the declining trend of local farmland amount, in order to meet the future challenges brought by urbanisation. Enhanced agricultural infrastructure, mechanised farming and guide from local government can help achieve this goal. This study provided new insights and more realistic scenarios for rural development in Southwest China. The research findings are expected to provide a better understanding to enable sustainable land use management in Southwest China.

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Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

Earth System Dynamics ◽

10.5194/esd-4-385-2013 ◽

2013 ◽

Vol 4 (2) ◽

pp. 385-407 ◽

Cited By ~ 73

Author(s):

M. Lindeskog ◽

A. Arneth ◽

A. Bondeau ◽

K. Waha ◽

J. Seaquist ◽

...

Keyword(s):

Land Use ◽

Vegetation Index ◽

Management Options ◽

Full Account ◽

Residue Removal ◽

Ecosystem Carbon ◽

Normalised Difference Vegetation Index ◽

Climate Forcings ◽

Vegetation Models ◽

And Function

Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991–1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2–6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971–2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land–atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr−1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the land–atmosphere carbon flux for the 20th century.

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Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

OENO One ◽

10.20870/oeno-one.2020.54.1.2557 ◽

2020 ◽

Vol 54 (2) ◽

pp. 189-197 ◽

Cited By ~ 2

Author(s):

Marco Sozzi ◽

Ahmed Kayad ◽

Francesco Marinello ◽

James Taylor ◽

Bruno Tisseyre

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Vegetation Indices ◽

Significance Test ◽

Support Vector ◽

Field Scale ◽

Mixed Pixel ◽

Normalised Difference Vegetation Index ◽

Aerial Vehicle ◽

Sentinel 2

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

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