scholarly journals Assessing Cropland Area in West Africa for Agricultural Yield Analysis

2018 ◽  
Vol 10 (11) ◽  
pp. 1785 ◽  
Author(s):  
Kaboro Samasse ◽  
Niall Hanan ◽  
Gray Tappan ◽  
Yacouba Diallo
Keyword(s):  
Food Security ◽  
Land Cover ◽  
West Africa ◽  
Spatial Resolution ◽  
Crop Production ◽  
Agricultural Landscape ◽  
Agricultural Land ◽  
Remotely Sensed Data ◽  
Cropland Area ◽  
Western Sahel

Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.

scholarly journals A Place-Based Approach to Agricultural Nonmaterial Intangible Cultural Ecosystem Service Values

2020 ◽  
Vol 12 (2) ◽  
pp. 699 ◽  
Author(s):  
Joy R. Petway ◽  
Yu-Pin Lin ◽  
Rainer F. Wunderlich
Keyword(s):  
Land Cover ◽  
Ecosystem Service ◽  
Agricultural Landscape ◽  
Agricultural Land ◽  
Environmental Data ◽  
Farming Practices ◽  
Environmental Characteristics ◽  
Cultural Ecosystem Service ◽  
Context Specific ◽  
Landscape Biodiversity

Though agricultural landscape biodiversity and ecosystem service (ES) conservation is crucial to sustainability, agricultural land is often underrepresented in ES studies, while cultural ES associated with agricultural land is often limited to aesthetic and tourism recreation value only. This study mapped 7 nonmaterial-intangible cultural ES (NICE) valuations of 34 rural farmers in western Taiwan using the Social Values for Ecosystem Services (SolVES) methodology, to show the effect of farming practices on NICE valuations. However, rather than a direct causal relationship between the environmental characteristics that underpin ES, and respondents’ ES valuations, we found that environmental data is not explanatory enough for causality within a socio-ecological production landscape where one type of land cover type (a micro mosaic of agricultural land cover) predominates. To compensate, we used a place-based approach with Google Maps data to create context-specific data to inform our assessment of NICE valuations. Based on 338 mapped points of 7 NICE valuations distributed among 6 areas within the landscape, we compared 2 groups of farmers and found that farmers’ valuations about their landscape were better understood when accounting for both the landscape’s cultural places and environmental characteristics, rather than environmental characteristics alone. Further, farmers’ experience and knowledge influenced their NICE valuations such that farm areas were found to be sources of multiple NICE benefits demonstrating that farming practices may influence ES valuation in general.

Analysis of potential flood damage on crops at global scale

2020 ◽  
Author(s):  
Antonio Annis ◽  
Davide Danilo Chiarelli ◽  
Fernando Nardi ◽  
Maria Cristina Rulli
Keyword(s):  
Food Security ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Hazard ◽  
Strong Support ◽  
Flood Damage ◽  
Natural Ecosystems ◽  
Remotely Sensed Data ◽  
Regulated Rivers ◽  
Cropland Area

<p>Most of the food production connected to crops is located in fluvial corridors because of their suitable morphology and fertile soils. The knowledge and large scale quantification of the agricultural resources at flood risk has a crucial importance for improving urban and regional planning. Recent advances in satellite derived products related to land use, digital terrain and hydrologic variables can give a strong support on extensive analyses on cropland areas in floodplains and their interactions with natural ecosystems and human activities. In this work, we present a global assessment of cropland at flood risk in terms of extension, productivity and the related calories adopting the Global Cropland Area Database (GCAD), the Global Floodplain Dataset (GFPLAIN250m), the Global flood hazard maps (GFHM) in conjunction with continental remotely-sensed data representing free flowing (versus artificially regulated) rivers and urban density maps. Spatially distributed and aggregated results of the research allow to identify the most critical areas in terms of food security and floods, thus allowing to support intervention strategies for food security management at large scale and for different socio-economic contexts.</p>

scholarly journals A COMPARATIVE ANALYSIS OF FIVE CROPLAND DATASETS IN AFRICA

2018 ◽  
Vol XLII-3 ◽  
pp. 1863-1870 ◽  
Author(s):  
Y. Wei ◽  
M. Lu ◽  
W. Wu
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Food Security ◽  
Comparative Analysis ◽  
Land Cover ◽  
Spatial Location ◽  
Error Matrix ◽  
Location Accuracy ◽  
Remote Sensing Imagery ◽  
Cropland Area

The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

scholarly journals Classification of various land features using RISAT-1 dual polarimetric data

2014 ◽  
Vol XL-8 ◽  
pp. 833-837 ◽  
Author(s):  
V. N. Mishra ◽  
P. Kumar ◽  
D. K. Gupta ◽  
R. Prasad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Uttar Pradesh ◽  
Land Cover Classification ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Sar Data ◽  
Land Cover Maps

Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.

scholarly journals MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1327-1334 ◽  
Author(s):  
W. Xu ◽  
B. Hays ◽  
R. Fayrer-Hosken ◽  
A. Presotto
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Area Under The Curve ◽  
Remotely Sensed Data ◽  
Distribution Models ◽  
African Savanna ◽  
Multi Scale ◽  
Land Cover Map ◽  
Better Than

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

scholarly journals Estimating landscape structure effects on pollination for management of agricultural landscapes

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ehsan Rahimi ◽  
Shahindokht Barghjelveh ◽  
Pinliang Dong
Keyword(s):  
Forest Fragmentation ◽  
Landscape Structure ◽  
Crop Production ◽  
Agricultural Landscape ◽  
Agricultural Land ◽  
Unit Area ◽  
Agricultural Landscapes ◽  
Edge Density ◽  
Forest Patches ◽  
New Forest

Abstract Background The growing human population around the world is creating an increased demand for food. In agricultural landscapes, forests are cleared and turned into agricultural land to produce more food. Increasing the productivity of agricultural land per unit area may prevent extreme forest degradation. Since many agricultural products are dependent on pollinators, it is possible to increase crop production by increasing the pollination rate in the agricultural landscapes. Pollinators are highly dependent on forest patches in agricultural landscapes. Therefore, by creating new forest patches around agricultural fields, we can increase the pollination rate, and thus the crop production. In this regard, estimating the effects of different scenarios of forest fragmentation helps us to find an optimized pattern of forest patches for increasing pollination in an agricultural landscape. Methods To investigate the effect of different forest fragmentation scenarios on pollination, we used simulated agricultural landscapes, including different forest proportions and degrees of fragmentation. Using landscape metrics, we estimated the relationship between pollination and landscape structure for each landscape. Results Our results showed that for increasing pollination, two significant factors should be considered: habitat amount and capacity of small patches to supply pollination. We found that when the capacity of small patches in supplying pollination was low, fragmented patterns of forest patches decreased pollination. With increasing capacity, landscapes with a high degree of forest fragmentation showed the highest levels of pollination. There was an exception for habitat amounts (the proportion of forest patches) less than 0.1 of the entire landscape where increasing edge density, aggregation, and the number of forest patches resulted in increasing pollination in all scenarios. Conclusion This study encourages agriculturists and landscape planners to focus on increasing crop production per unit area by pollinators because it leads to biodiversity conservation and reduces socio-economic costs of land-use changes. We also suggest that to increase pollination in agricultural landscapes by creating new forest patches, special attention should be paid to the capacity of patches in supporting pollinators.

scholarly journals Local Perception of Drivers of Land-Use and Land-Cover Change Dynamics across Dedza District, Central Malawi Region

2019 ◽  
Vol 11 (3) ◽  
pp. 832 ◽  
Author(s):  
Maggie G. Munthali ◽  
Nerhene Davis ◽  
Abiodun M. Adeola ◽  
Joel O. Botai ◽  
Jonathan M. Kamwi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Management ◽  
Agricultural Land ◽  
Management Strategies ◽  
Rural Livelihoods ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Barren Land ◽  
Lulc Changes

Research on Land Use and Land Cover (LULC) dynamics, and an understanding of the drivers responsible for these changes, are very crucial for modelling future LULC changes and the formulation of sustainable and robust land-management strategies and policy decisions. This study adopted a mixed method consisting of remote sensing and Geographic Information System (GIS)-based analysis, focus-group discussions, key informant interviews, and semi-structured interviews covering 586 households to assess LULC dynamics and associated LULC change drivers across the Dedza district, a central region of Malawi. GIS-based analysis of remotely sensed data revealed that barren land and built-up areas extensively increased at the expense of agricultural and forest land between 1991 and 2015. Analysis of the household-survey results revealed that the perceptions of respondents tended to validate the observed patterns during the remotely sensed data-analysis phase of the research, with 57.3% (n = 586) of the respondents reporting a decline in agricultural land use, and 87.4% (n = 586) observing a decline in forest areas in the district. Furthermore, firewood collection, charcoal production, population growth, and poverty were identified as the key drivers of these observed LULC changes in the study area. Undoubtedly, education has emerged as a significant factor influencing respondents’ perceptions of these drivers of LULC changes. However, unsustainable LULC changes observed in this study have negative implications on rural livelihoods and natural-resource management. Owing to the critical role that LULC dynamics play to rural livelihoods and the ecosystem, this study recommends further research to establish the consequences of these changes. The present study and future research will support decision makers and planners in the design of tenable and coherent land-management strategies.

scholarly journals Land Cover Changes and Flows in the Polish Baltic Coastal Zone: A Qualitative and Quantitative Approach

2020 ◽  
Vol 12 (13) ◽  
pp. 2088 ◽  
Author(s):  
Elzbieta Bielecka ◽  
Agnieszka Jenerowicz ◽  
Krzysztof Pokonieczny ◽  
Sylwia Borkowska
Keyword(s):  
Land Cover ◽  
Coastal Zone ◽  
Agricultural Land ◽  
Accurate Information ◽  
Land Cover Changes ◽  
Remotely Sensed Data ◽  
Baltic Coast ◽  
Spatiotemporal Trends ◽  
Artificial Surface ◽  
The Baltic

Detecting land cover changes requires timely and accurate information, which can be assured by using remotely sensed data and Geographic Information System(GIS). This paper examines spatiotemporal trends in land cover changes in the Polish Baltic coastal zone, especially the urbanisation, loss of agricultural land, afforestation, and deforestation. The dynamics of land cover change and its impact were discussed as the major findings. The analysis revealed that land cover changes on the Polish Baltic coast have been consistent throughout the 1990–2018 period, and in the consecutive inventories of land cover, they have changed faster. As shown in the research, the area of agricultural land was subject to significant change, i.e., about 40% of the initial 8% of the land area in heterogeneous agriculture was either developed or abandoned at about equal rates. Next, the steady growth of the forest and semi-natural area also changed the land cover. The enlargement of the artificial surface was the third observed trend of land cover changes. However, the pace of land cover changes on the Baltic coast is slightly slower than in the rest of Poland and the European average. The region is very diverse both in terms of land cover, types of land transformation, and the pace of change. Hence, the Polish national authorities classified the Baltic coast as an area of strategic intervention requiring additional action to achieve territorial cohesion and the goals of sustainable development.

scholarly journals Sentinel-2 Data for Land Cover/Use Mapping: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2291 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Serajis Salekin ◽  
Vincent R. Nyirenda ◽  
Yuji Murayama ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Positive Impact ◽  
European Space Agency ◽  
Remotely Sensed ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Free Access ◽  
Remotely Sensed Data ◽  
Sentinel 2

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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