Experience using the NDVI normalized difference vegetation index for monitoring Polesye agricultural land based on multispectral Ikonos satellite imaging data

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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Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16234845 ◽

2019 ◽

Vol 16 (23) ◽

pp. 4845

Author(s):

Bipin Acharya ◽

Wei Chen ◽

Zengliang Ruan ◽

Gobind Pant ◽

Yin Yang ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Scrub Typhus ◽

Control Strategies ◽

National Level ◽

Spatial Prediction ◽

Health Concern ◽

Environmental Suitability ◽

And Control

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

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Integrating an Expert System, GIS, and Satellite Remote Sensing to Evaluate Land Suitability for Sustainable Tea Production in Bangladesh

Remote Sensing ◽

10.3390/rs12244136 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4136

Author(s):

Animesh Chandra Das ◽

Ryozo Noguchi ◽

Tofael Ahamed

Keyword(s):

Remote Sensing ◽

Land Use ◽

Satellite Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Multicriteria Analysis ◽

Land Evaluation ◽

Land Policy ◽

Tea Production

Land evaluation is important for assessing environmental limitations that inhibit higher yield and productivity in tea. The aim of this research was to determine the suitable lands for sustainable tea production in the northeastern part of Bangladesh using phenological datasets from remote sensing, geospatial datasets of soil–plant biophysical properties, and expert opinions. Sentinel-2 satellite images were processed to obtain layers for land use and land cover (LULC) as well as the normalized difference vegetation index (NDVI). Data from the Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. Other vector and raster layers of edaphic, climatic parameters, and vegetation indices were processed in ArcGIS 10.7.1® software. Finally, suitability classes were determined using weighted overlay of spatial analysis based on reclassified raster layers of all parameters along with the results from multicriteria analysis. The results of the study showed that only 41,460 hectares of land (3.37% of the total land) were in the highly suitable category. The proportions of moderately suitable, marginally suitable, and not suitable land categories for tea cultivation in the Sylhet Division were 9.01%, 49.87%, and 37.75%, respectively. Thirty-one tea estates were located in highly suitable areas, 79 in moderately suitable areas, 24 in marginally suitable areas, and only one in a not suitable area. Yield estimation was performed with the NDVI (R2 = 0.69, 0.66, and 0.67) and the LAI (R2 = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively. This research suggests that satellite remote sensing and GIS application with the analytical hierarchy process (AHP) could be used by agricultural land use planners and land policy makers to select suitable lands for increasing tea production.

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Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

Remote Sensing ◽

10.3390/rs12132113 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2113

Author(s):

Dan Wanyama ◽

Nathan J. Moore ◽

Kyla M. Dahlin

Keyword(s):

Climate Variability ◽

Time Scales ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Multiple Time Scales ◽

Multiple Time ◽

Local Climate ◽

Sen’S Slope ◽

Mount Elgon

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

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Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

Remote Sensing ◽

10.3390/rs13040568 ◽

2021 ◽

Vol 13 (4) ◽

pp. 568

Author(s):

David Andrés Rivas-Tabares ◽

Antonio Saa-Requejo ◽

Juan José Martín-Sotoca ◽

Ana María Tarquis

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Precipitation Pattern ◽

Scaling Properties ◽

Vegetation Growth ◽

Factors Affecting ◽

Series Analysis ◽

Climate Interactions

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

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Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

Remote Sensing ◽

10.3390/rs13040681 ◽

2021 ◽

Vol 13 (4) ◽

pp. 681 ◽

Cited By ~ 1

Author(s):

Sergio Morell-Monzó ◽

María-Teresa Sebastiá-Frasquet ◽

Javier Estornell

Keyword(s):

Random Forest ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Majority Voting ◽

Land Abandonment ◽

Land Use Classification ◽

Blue Band ◽

Lower Accuracy

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.

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Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

Sustainability ◽

10.3390/su10124363 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4363 ◽

Cited By ~ 1

Author(s):

Patricia Arrogante-Funes ◽

Carlos Novillo ◽

Raúl Romero-Calcerrada

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Land Cover Changes ◽

Mountainous Area ◽

Statistical Association ◽

Mountain Areas ◽

Mountain Ranges ◽

Statistical Trends

Currently, there exists growing evidence that warming is amplified with elevation resulting in rapid changes in temperature, humidity and water in mountainous areas. The latter might result in considerable damage to forest and agricultural land cover, affecting all the ecosystem services and the socio-economic development that these mountain areas provide. The Mediterranean mountains, moreover, which host a high diversity of natural species, are more vulnerable to global change than other European ecosystems. The protected areas of the mountain ranges of peninsular Spain could help preserve natural resources and landscapes, as well as promote scientific research and the sustainable development of local populations. The temporal statistical trends (2001–2016) of the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) interannual dynamics are analyzed to explore whether the NDVI trends are found uniformly within the mountain ranges of mainland Spain (altitude > 1000 m), as well as in the protected or non-protected mountain areas. Second, to determine if there exists a statistical association between finding an NDVI trend and the specific mountain ranges, protected or unprotected areas are studied. Third, a possible association between cover types in pure pixels using CORINE (Co-ordination of Information on the Environment) land cover cartography is studied and land cover changes between 2000 and 2006 and between 2006 and 2012 are calculated for each mountainous area. Higher areas are observed to have more positive NDVI trends than negative in mountain areas located in mainland Spain during the 2001–2016 period. The growing of vegetation, therefore, was greater than its decrease in the study area. Moreover, differences in the size of the area between growth and depletion of vegetation patterns along the different mountains are found. Notably, more negatives than expected are found, and fewer positives are found than anticipated in the mountains, such as the Cordillera Cantábrica (C.Cant.) or Montes de Murcia y Alicante (M.M.A). Quite the reverse happened in Pirineos (Pir.) and Montes de Cádiz y Málaga (M.C.M.), among others. The statistical association between the trends found and the land cover types is also observed. The differences observed can be explained since the mountain ranges in this study are defined by climate, land cover, human usage and, to a small degree, by land cover changes, but further detailed research is needed to get in-depth detailed conclusions. Conversely, it is found that, in protected mountain areas, a lower NDVI pixels trend than expected (>20%) occurs, whereas it is less than anticipated in unprotected mountain areas. This could be caused by management and the land cover type.

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Multi-criteria modelling of drought: a study of Brandenburg Federal State, Germany

Modeling Earth Systems and Environment ◽

10.1007/s40808-021-01197-2 ◽

2021 ◽

Author(s):

Christopher Ihinegbu ◽

Taiwo Ogunwumi

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Federal State ◽

Landsat 8 ◽

Gis And Remote Sensing ◽

Drought Assessment

AbstractDrought is the absence or below-required supply of precipitation, runoff and or moisture for an extended time period. Modelling drought is relevant in assessing drought incidence and pattern. This study aimed to model the spatial variation and incidence of the 2018 drought in Brandenburg using GIS and remote sensing. To achieve this, we employed a Multi-Criteria Approach (MCA) by using three parameters including Precipitation, Land Surface Temperature and Normalized Difference Vegetation Index (NDVI). We acquired the precipitation data from Deutsche Wetterdienst, Land Surface Temperature and NDVI from Landsat 8 imageries on the USGS Earth Explorer. The datasets were analyzed using ArcGIS 10.7. The information from these three datasets was used as parameters in assessing drought prevalence using the MCA. The MCA was used in developing the drought model, ‘PLAN’, which was used to classify the study area into three levels/zones of drought prevalence: moderate, high and extreme drought. We went further to quantify the agricultural areas affected by drought in the study area by integrating the land use map. Results revealed that 92% of the study area was severely and highly affected by drought especially in districts of Oberhavel, Uckermark, Potsdam-Staedte, and Teltow-Flaeming. Finding also revealed that 77.54% of the total agricultural land falls within the high drought zones. We advocated for the application of drought models (such as ‘PLAN’), that incorporates flexibility (tailoring to study needs) and multi-criteria (robustness) in drought assessment. We also suggested that adaptive drought management should be championed using drought prevalence mapping.

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Analisa Penyebaran Potensi Kekeringan Lahan di Kabupaten Rembang

Majalah Geografi Indonesia ◽

10.22146/mgi.30213 ◽

2020 ◽

Vol 33 (2) ◽

Author(s):

Martza Swastikasari ◽

Natania Frislya Nanulaitta

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Satellite Image ◽

Infiltration Rate ◽

High Water ◽

Rainfall Data ◽

Water Infiltration ◽

Central Java ◽

Water Holding

Kekeringan lahan yang melanda suatu daerah menimbulkan dampak yang besar terhadap produktivitas lahan pertanian. Terjadinya kekeringan ini disebabkan oleh defisit air akibat kurangnya hujan yang jatuh, laju infiltrasi air yang tinggi serta jenis tanaman yang tidak sesuai dengan ketersediaan air. Untuk meminimalkan dampak yang terjadi akibat kekeringan lahan maka perlu dilakukan antisipasi dengan mengetahui defisit dan surflus air lahan melalui data curah hujan serta kemampuan tanah menahan air (Water Holding Capasity). Oleh sebab itu, dalam penelitian ini penulis mencoba untuk menganalisa penyebaran potensi penyebaran kekeringan di wilayah Kabupaten Rembang Propinsi Jawa Tengah. Parameter yang didapat yaitu interpretasi dari citra satelit lansat 8 (OLI), data statistik Kabupaten Rembang, dan data curah hujan. Didalam penelitian ini juga penulis menggunakan indeks vegetasi NDVI (Normalized Difference Vegetation Index) dan SAVI (Soil-Adjusted Vegetation Index)yang dapat menghasilkan rata-rata luas wilayah potensi kekeringan di masing-masing kecamatan pada Kabupaten Rembang. Land drought that hit a region has a great impact on the productivity of agricultural land. The occurrence of this drought is caused by water deficit due to lack of falling rain, high water infiltration rate and types of plants that are not in accordance with the availability of water. To minimize the impacts caused by land drought, it is necessary to be anticipated by knowing the deficit and land water surfs through rainfall data and the ability of water holding capasity. Therefore in this study the authors try to analyze the spread of potential spread of drought in rembang district of central java province. The parameters obtained are the interpretation of satellite image 8 (OLI), statistical data of Rembang Regency, and rainfall data. In this study, the authors used the NDVI (Normalized Difference Vegetation Index) and SAVI (Soil-Adjusted Vegetation Index) vegetation index which can produce the average of drought potential areas in each sub-district in Rembang district.

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Measuring the Loss of Arable and Rural Lands on the Maltese Islands through Satellite Images

MCAST Journal of Applied Research & Practice ◽

10.5604/01.3001.0014.4398 ◽

2020 ◽

Vol 4 (1) ◽

pp. 52-68

Author(s):

Steve Zerafa

Keyword(s):

Land Use ◽

Urban Growth ◽

Rural Areas ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Water Flooding ◽

Vegetation Density ◽

Maltese Islands

The Maltese Islands went through a rapid urban growth and increase in population. Such trends normally contribute to the loss of agricultural land, trees, soil and rural land. Urban growth is often responsible for a variety of urban environmental issues: Decreased air quality, increased runoff and subsequent water flooding, increased local temperature, losses of agricultural land and deterioration of water table. During such times, it is crucial to monitor the use of land resources, understand the changes of biodiversity and ecosystems, and ensure the long-term productive potential of soil, land and plants. Although the islands are small in size, such a monitoring task is quite challenging due to the effects of weather on the islands, the dynamics of the vegetation, and the continued activities of locals all across the islands. In this context, geospatial technologies and remote sensing techniques could serve as an essential tool for the analysis of land use and detecting changes occurring within the ecosystems. This study attempts to assess the land use change detection at a pixel level and highlight the vegetation density, and workout the loss of vegetative in arable and rural areas across the islands during the years 2015 to 2019. The created models are derived from the observation of the Normalized Difference Vegetation Index (NDVI) as obtained by Sentinel-2 satellite images. The results showed that from Spring 2017 to Spring 2019, the islands experienced a 2.45km² reduction of green vegetation colour. Over a period of 4 years the islands experienced a 1.25km² erosion of arable and rural lands. Among other reasons, this loss is the result of more development and the extension of the urbanization zones.

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