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

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.

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

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
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.

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2012 ◽  
Vol 9 (8) ◽  
pp. 10149-10205 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Nitrogen Content ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Leaf Nitrogen ◽  
Nitrogen Pools ◽  
Canopy Nitrogen ◽  
Use Categories

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10–20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five European landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r2=0.7; rmse = 0.73), canopy chlorophyll content (r2=0.51; rmse = 439 mg m−2) and canopy nitrogen content (r2 = 0.53; rmse = 2.21 g m−2). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, barley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10 × 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (CHLc) for the five landscapes could be predicted with improved accuracy (LAI rmse = 0.59; CHLc rmse = 346 g m−2; Ncrmse = 1.49 g m−2). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 t km−2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl.

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

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.

scholarly journals COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals Management of current land use of perennial industrial crops by NDVI index: A case study in Chu Se District, Gia Lai Province, Vietnam

2014 ◽  
Vol 6 (2) ◽  
pp. 159-164
Author(s):  
Hoang Khanh Linh Nguyen ◽  
Bich Ngoc Nguyen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Modern Technology ◽  
Landsat 8 ◽  
Viet Nam ◽  
Gis Technology ◽  
Industrial Plants ◽  
Ndvi Index

Remote sensing and Geographic Information System (GIS) - an effective tool for managing natural resources, is quite common application in establishing thematic maps. However, the application of this modern technology in natural resource management has not yet been popular in Vietnam, particularly mapping the land use/cover. Currently, land use/cover map is constructed as traditional methods and gets limitations of management counting due to time-consuming for mapping and synthesis the status of land use/cover. Hence, information on the map is often outdated and inaccurate. The main objective of this study is to upgrade the accuracies in mapping current perennial crops in Chu Se District, Gia Lai Province in Vietnam by interpreted NDVI index (Normalized Difference Vegetation Index) from Landsat 8-OLI (Operational Land Imager). The results of study is satisfied the urgent of practical requirement and scientific research. There are 3 types of perennial industrial plants in the study area including rubber, coffee, and pepper, in which most coffee is grown, with an area of over 10,000 hectares. The results also show that integration of remote sensing and GIS technology enables to map current management and distribution of perennial industrial plants timely and accurately. This application is fully consistent with the trend of the world, and in accordance with regulations of established land use/cover map, and the process could be applied at other districts /towns or in higher administrative units. Viễn thám và hệ thông tin địa lý (GIS) là công cụ hữu hiệu để quản lý tài nguyên thiên nhiên, được ứng dụng khá phổ biến để thành lập các loại bản đồ. Tuy nhiên, việc áp dụng công nghệ hiện đại này trong lĩnh vực quản lý tài nguyên thiên nhiên ở Việt Nam chưa phổ biến, nhất là công tác xây dựng bản đồ hiện trạng sử dụng/độ phủ đất. Việc xây dựng bản đồ hiện trạng hiện nay vẫn theo phương pháp truyền thống, thường gặp nhiều hạn chế do thời gian tổng hợp và xây dựng bản đồ hiện trạng kéo dài, dẫn đến thông tin trên bản đồ bị lạc hậu và không chính xác. Mục tiêu chính của nghiên cứu này là nâng cao độ chính xác kết quả giải đoán ảnh viễn thám Landsat 8 bằng chỉ số NDVI (chỉ số khác biệt thực vật) để thành lập bản đồ hiện trạng sử dụng đất cây công nghiệp lâu năm ở huyện Chư Sê, tỉnh Gia Lai, Việt Nam. Từ đó quản lý hiện trạng sử dụng loại đất này phù hợp yêu cầu cấp bách thực tiễn sản xuất và nghiên cứu khoa học. Kết quả của nghiên cứu cho thấy có 3 loại hình cây công nghiệp trên địa bàn nghiên cứu gồm cây cao su, cà phê và hồ tiêu, trong đó cây cà phê được trồng nhiều nhất, với diện tích hơn 10.000 ha. Nghiên cứu cũng cho thấy, tích hợp công nghệ viễn thám và GIS cho phép quản lý hiện trạng và phân bố cây công nghiệp trong không gian một cách hiệu quả và nhanh chóng. Ứng dụng này hoàn toàn phù hợp với xu hướng của thế giới, đồng thời theo đúng quy định thành lập bản đồ hiện trạng sử dụng đất, và quy trình này có thể thực hiện được ở cấp huyện/thị xã hoặc đơn vị hành chính cấp cao hơn.

scholarly journals Land Use Change in Buffer Zone of Chitwan National Park, Nepal between 1978 and 1999

1970 ◽  
Vol 16 ◽  
pp. 79-86 ◽  
Author(s):  
N. G. Baidya ◽  
D. R. Bhuju ◽  
P. Kandel
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
National Park ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Buffer Zone ◽  
Community Forest Management ◽  
Vegetation Density ◽  
Chitwan National Park ◽  
Total Study Area

Land use dynamics in the Buffer Zone of Chitwan National Park was assessed by integrated use of Remote Sensing and Geographic Information System covering 1978, 1992 and 1999. Among all land-use type, forest was the most dominating land-use in 1978 covering 45.33% of total study area. However, the agricultural land became the most dominating land-use since 1992. The study found that since 1978 to 1999 there was increase in agricultural land by 67.28 km2, built-up area by 0.81 km2 and shrub-land by 7.68 km2. Whereas, the forest decreased by 62.23 km2, grassland by 11.38 km2 and water bodies by 2.16 km2. Vegetation density analysis using Normalized Difference Vegetation Index (NDVI) revealed that the densely vegetated area decreased by 5.03 km2 in a time-span of less than 10 years. Despite the successful community forest management in the BZ of CNP, this study showed a decline in densely forested area. Key words: GIS, Land use types, management, Remote sensing, Vegetation. DOI: 10.3126/eco.v16i0.3478ECOPRINT 16: 79-86, 2009

scholarly journals Application of High-Resolution Remote-Sensing Data for Land Use Land Cover Mapping of University Campus

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Irwan Ary Dharmawan ◽  
Muhammad Ario Eko Rahadianto ◽  
Edward Henry ◽  
Cipta Endyana ◽  
Muhammad Aufaristama
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Surface Runoff ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
University Campus

The study of Land Use Land Cover (LULC) is essential to understanding how land has been altered in recent years and what has caused the processes behind the change. This is significant for the future development of the area, particularly on the campus of the Universitas Padjadjaran Jatinangor. The purpose of this study was to apply remote-sensing techniques to map a university campus and vicinity by comparing the area of urban green space (UGS) and floor area ratios (FARs) of the campus in 2015 and 2017. Additionally, surface runoff analysis was also conducted. For our research, we used WorldView-2’s high-resolution satellite imagery with a resolution of 0.46 m in the Universitas Padjadjaran (Padjadjaran University, or Unpad) Jatinangor campus, Jawa Barat, Indonesia. Our approach was to interpret the imagery by running the normalized difference vegetation index (NDVI) to distinguish UGS and FAR and using digital elevation model (DEM) interferometric synthetic aperture radar (SAR) data with hydrologic analysis to identify the direction of surface runoff. The results obtained are as follows: the UGS remained more extensive compared with FAR, but the difference decreased over time owing to infrastructure development. Surface runoff has tended to flow toward the southeast in direct relation to the slope configuration.

scholarly journals Aplicação do Índice da Vegetação por Diferença Normalizada (NDVI) à Análise Multitemporal da Dinâmica de Áreas Agrícolas no Alto Curso da Bacia do Rio Uberabinha, Minas Gerais Application of the (...)

2015 ◽  
Vol 25 (44) ◽  
pp. 149-164
Author(s):  
Vanderlei De Oliveira Ferreira ◽  
Mirella Velluma Portilho Magalhães
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Visual Interpretation ◽  
Land Occupation ◽  
Reconstruction Process ◽  
Seasonal Scale ◽  
Agricultural Areas

O mapeamento do uso do solo é essencial para acompanhamento do processo de reconstrução continuada da paisagem, sendo útil para definição de estratégias de utilização dos recursos naturais. O presente artigo relata pesquisa dedicada a inventariar e compreender a dinâmica do uso agrícola do solo sob uma perspectiva multitemporal (escala sazonal) no alto curso da bacia do rio Uberabinha, no Triângulo Mineiro, a montante da sede municipal de Uberlândia. Utilizou-se a técnica do NDVI (Normalized Difference Vegetation Index) devido à sua aptidão para levantamento de áreas agrícolas. O mapeamento foi elaborado por meio da interpretação visual, recorrendo-se às imagens do sensor LANDSAT 5 e ResourceSat-1, com a composição colorida 4R5G3B. Foi possível diferenciar os diversos estádios fenológicos da cobertura vegetal, percebendo situações de manejo e forma de ocupação do solo em diferentes épocas do ano. Observa-se, por exemplo, que não há recorrência ao pousio da terra entre uma cultura e outra. Os produtores adotam o método de plantio direto, intercalando culturas, além de forrageiras e leguminosas para melhorar a qualidade nutricional do solo.Palavras chave: Mapeamento; Sensoriamento Remoto; Uso agrícola do solo; Escala sazonal.AbstractThe mapping of the land use is essential for accompaniment of the reconstruction process continued of landscape, being useful for define strategies of utilization of the natural resources. This article reports the research dedicated to inventory and understand the dynamics of agricultural land use under a multitemporal perspective (seasonal scale) in the high course of the basin of the Uberabinha river, in the Triângulo Mineiro, the upstream of the municipal headquarters of Uberlândia. We used the technique of NDVI (Normalized Difference Vegetation Index) due to its aptitude for survey of agricultural areas. The mapping was prepared by visual interpretation, resorting to images of the sensor LANDSAT 5 and ResourceSat-1, with colorful makeup 4R5G3B. It was possible to differentiate the several phenological stages of the vegetation cover, realizing management situations and forms of land occupation in differents epochs of the year. It is observed that there is no recurrence to fallow of the land between one culture and another. The producers adopt the method of tillage, interspersing cultures, besides forages and legumes for improve the nutritional quality of the soil. Keywords: Mapping; Remote Sensing; Agricultural land use; Seasonal scale. 

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