Your search found 23 records
1 Latifovic, R.; Olthof, I. 2004. Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote Sensing of Environment, 90:153-165.
(Location: IWMI-HQ Call no: P 7650 Record No: H039396)
2 Braswell, B. H.; Hagen, S. C.; Frolking, S. E.; Salas, W. A. 2003. A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: Application in the Brazilian Amazon region. Remote Sensing of Environment, 87:243-256.
(Location: IWMI-HQ Call no: P 7651 Record No: H039397)
3 Simic, A.; Chen, J. M.; Liu, J.; Csillag, F. 2004. Spatial scaling of net primary productivity using subpixel information. Remote Sensing of Environment, 93:246-258.
Remote sensing ; Ecosystems ; Forests ; Soil water ; Evapotranspiration ; Land cover mapping / Canada / Ontario
(Location: IWMI-HQ Call no: P 7656 Record No: H039402)
4 Yemefack, M.; Bijker, W.; De Jong, S. M. 2005. Investigating relationships between Landsat - 7 ETM data and spatial segregation of LULC types under shifting agriculture in southern Cameroon. International Journal of Applied Earth Observation and Geoinformation, 7(4):96-112.
(Location: IWMI-HQ Call no: P 7666 Record No: H039416)
5 Gamage, M. S. D. Nilantha; Ahmad, Mobin-ud-Din; Turral, Hugh. 2007. Semi-supervised technique to retrieve irrigated crops from Landsat ETM+ imagery for small fields and mixed cropping systems of South Asia. International Journal of Geoinformatics, 3(1)::45-53.
Remote sensing ; Satellite surveys ; Cropping Systems ; Land use ; Land cover mapping / Pakistan / Punjab / Rechna Doab
(Location: IWMI HQ Call no: IWMI 631.7.1 G730 GAM Record No: H039994)
6 Biradar, Chandrashekhar; Thenkabail, Prasad; Islam, Aminul; Anputhas, Markandu; Tharme, Rebecca; Vithanage, Jagath; Alankara, Ranjith; Gunasinghe, Sarath. 2007. Establishing the best spectral bands and timing of imagery for land use-land cover (LULC) class separability using Landsat ETM+ and Terra MODIS data. Canadian Journal of Remote Sensing, 33(5):431-444.
Remote sensing ; Land use ; Land cover mapping ; Irrigated farming ; Irrigation programs / Sri Lanka / Uda Walawe River Basin
(Location: IWMI HQ Call no: IWMI 333.918 G744 BIR Record No: H040453)
7 Canisius, F. 2009. Multiangle spectral measurements: a way to distinguish cropping areas. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.393-408. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042431)
8 Rosenqvist, A.; Shimada, M. (Eds.) 2010. Global environmental monitoring by ALOS PALSAR: science results from the ALOS Kyoto and Carbon Initiative. Tsukuba, Ibaraki, Japan: Japan Aerospace Expoloration Agency. 87p.
Environmental monitoring ; Satellite imagery ; Forests ; Deforestation ; Mapping ; Watersheds ; Land cover mapping ; Deserts ; Wetlands ; Wildlife ; Nature conservation ; Habitats ; Flooding ; River basins ; Mangroves ; Peatlands ; Rice ; Climate change / Africa / Malawi / South Africa / Mozambique / USA / Brazil / Sweden / Canada / Australia / Asia / South East Asia / Borneo / Indonesia / Sumatra / Vietnam / Siberia / South East Asia / Amazon / Xingu Watershed / Greater Mekong Basin / Queensland / Nile River / Lake Urema / Congo River Basin / Sahara / Alaska
(Location: IWMI HQ Call no: e-copy only Record No: H043187)
http://www.eorc.jaxa.jp/ALOS/en/kyoto/ref/KC-Booklet_2010_comp.pdf
https://vlibrary.iwmi.org/pdf/H043187.pdf
https://vlibrary.iwmi.org/pdf/H043187.pdf
(17.26 MB) (17.26 MB)
This booklet presents results obtained within the ALOS Kyoto & Carbon (K&C) Initiative. The Initiative builds on the experience gained from the JERS-1 Global Rain Forest and Boreal Forest Mapping (GRFM/GBFM) projects, in which SAR data from the JERS-1 satellite were used to generate image mosaics over the entire tropical and boreal zones of Earth. While the GRFM/GBFM projects were undertaken already in the mid 1990's, they demonstrated the utility of L-band SAR data for mapping and monitoring forest and wetland areas and the importance of providing spatially and temporally consistent satellite acquisitions for regional-scale monitoring and surveillance. The ALOS K&C Initiative is set out to suppor t data and information needs raised by international environmental Conventions, Carbon cycle science and Conservation of the environment. The project is led by JAXA EORC and supported by an international Science Team consisting of some 25 research groups from 14 countries. The objective of the ALOS K&C Initiative is to develop regional-scale applications and thematic products derived primarily from ALOS PALSAR data that can be used to meet the specific information requirements relating to Conventions, Carbon and Conservation. The Initiative is undertaken within the context of three themes which relate to three specific global biomes; Forests, Wetlands and Deserts. A fourth theme deals with the generation of continental-scale ALOS PALSAR image mosaics. Each theme has identified key products that are generated from the PALSAR data including land cover, forest cover and forest change maps, biomass and structure (Forests), wetlands inventory and change (Wetlands) and freshwater resources (Deserts). Each of these products are generated using a combination of PALSAR, in situ and ancillary datasets. The mosaic data sets and thematic products generated within the Initiative are available to the public at the K&C homepage at JAXA EORC: http://www.eorc.jaxa.jp/ALOS/en/kyoto/kyoto_index.html
9 Mango, L. M.; Melesse, A. M.; McClain, M. E.; Gann, D.; Setegn, S. G. 2011. Hydro-meteorology and water budget of the Mara River Basin under land use change scenarios. In Melesse, A. M. (Ed.). Nile River Basin: hydrology, climate and water use. Dordrecht, Netherlands: Springer. pp.39-68.
Hydrometeorology ; Water budget ; Water balance ; River basins ; International waters ; Land use ; Land cover mapping ; Hydrology ; Models / Kenya / Tanzania / Mara River Basin
(Location: IWMI HQ Call no: 551.483 G136 MEL Record No: H044022)
10 Mondal, S.; Jeganathan, C.; Amarnath, Giriraj; Pani, Peejush. 2017. Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring. GIScience and Remote Sensing, 54(2):202-229. [doi: https://doi.org/10.1080/15481603.2017.1286726]
Climate change ; Drought ; Clouds ; Noise ; Monitoring ; Vegetation ; Satellite observation ; Satellite imagery ; Land cover mapping ; Remote sensing ; Spatial distribution ; Models ; Statistical methods ; Performance evaluation ; Homogenization ; Agriculture / Sri Lanka
(Location: IWMI HQ Call no: e-copy only Record No: H048010)
Moderate Resolution Imaging Spectro-radiometer (MODIS) time-series Normalized Differential Vegetation Index (NDVI) products are regularly used for vegetation monitoring missions and climate change analysis. However, satellite observation is affected by the atmospheric condition, cloud state and shadows introducing noise in the data. MODIS state flag helps in understanding pixel quality but overestimates the noise and hence its usability requires further scrutiny. This study has analyzed MODIS MOD09A1 annual data set over Sri Lanka. The study presents a simple and effective noise mapping method which integrates four state flag parameters (i.e. cloud state, cloud shadow, cirrus detected, and internal cloud algorithm flag) to estimate Cloud Possibility Index (CPI). Usability of CPI is analyzed along with NDVI for noise elimination. Then the gaps generated due to noise elimination are reconstructed and performance of the reconstruction model is assessed over simulated data with five different levels of random gaps (10–50%) and four different statistical measures (i.e. Root mean square error, mean absolute error, mean bias error, and mean absolute percentage error). The sample-based analysis over homogeneous and heterogeneous pixels have revealed that CPI-based noise elimination has increased the detection accuracy of number of growing cycle from 45–60% to 85–95% in vegetated regions. The study cautions that usage of time-series NDVI data without proper cloud correction mechanism would result in wrong estimation about spatial distribution and intensity of drought, and in our study 50% of area is wrongly reported to be under drought though there was no major drought in 2014.
11 Kadyampakeni, Davie M.; Mul, Marloes L.; Obuobie, E.; Appoh, Richard; Owusu, Afua; Ghansah, Benjamin; Boakye-Acheampong, Enoch; Barron, Jennie. 2017. Agro-climatic and hydrological characterization of selected watersheds in northern Ghana. Colombo, Sri Lanka: International Water Management Institute (IWMI). 40p. (IWMI Working Paper 173) [doi: https://doi.org/10.5337/2017.209]
Watersheds ; Agricultural production ; Intensification ; Agroclimatology ; Hydrology ; Analytical method ; Agronomic practices ; Water balance ; Water quality ; Water management ; Water deficit ; Climatic factors ; pH ; Electrical conductivity ; Soil texture ; Soil quality ; Soil sampling ; Soil fertility ; Land cover mapping ; Land use ; Rain ; Temperature ; Evapotranspiration ; Farmers ; Wet season ; Dry season ; Reservoir storage ; Wells ; Rivers ; Irrigation schemes ; Catchment areas ; Cropping systems ; Crop production ; Meteorological stations ; Cation exchange capacity / Ghana
(Location: IWMI HQ Call no: IWMI Record No: H048209)
(1 MB)
This paper provides the climatic and biophysical context of three watersheds in northern Ghana. The objective of the study is to describe the agro-climatic and hydrological features of the watersheds from a landscape perspective. The analyses show that water surplus occurs about 3 months in a year, with only one month providing a significant surplus. Small-scale irrigation is, therefore, carried out in the dry months between November and June. The quality of water used for irrigation from wells, reservoirs and rivers is good for irrigation and domestic purposes. The soil chemical parameters across the study sites show that the soils are suitable for irrigation and crop system intensification, although it requires substantial fertilizer inputs. The paper concludes that there are opportunities from both a soil quality and water availability perspective to enhance sustainable intensification through small- and medium-scale irrigation in the selected watersheds.
12 Tiwari, K.; Goyal, R.; Sarkar, A. 2018. GIS-based methodology for identification of suitable locations for rainwater harvesting structures. Water Resources Management, 32(5):1811-1825. [doi: https://doi.org/10.1007/s11269-018-1905-9]
Rainwater ; Water harvesting ; GIS ; Remote sensing ; Surface runoff ; Drainage systems ; Estimation ; Land use mapping ; Land cover mapping ; Soil types ; Slopes ; Models / India / Rajasthan / Alwar
(Location: IWMI HQ Call no: e-copy only Record No: H048510)
(4.10 MB)
Presently, the water resources across the world are being continuously depleted. It is essential to find sustainable solutions for this shortage of water. Rainwater harvesting is one such promising solution to this problem. This paper presents a new GIS-based methodology to identify suitable locations for rainwater harvesting structures using only freely available imageries/remote sensing data and data from other sources. The methodology has been developed for the semi-arid environment of Khushkhera-Bhiwadi-Neemrana Investment Region (KBNIR) in Alwar district of Rajasthan. For identifying locations suitable for rainwater harvesting structures, the layers of surface elevation (ASTER-DEM), landuse/landcover, soil map, drainage map and depression map are used and further analyzed for their depression volume, and availability of surface runoff using Soil Conservation Service - Curve Number (SCS-CN) method. Based on the proposed criteria total seven locations were identified, out of which two locations are excellent; three locations are good, (if provisions of overflow structure are made for them) and two locations are not suitable for rain water harvesting. The total rainwater harvesting potential of the study area is 54.49 million cubic meters which is sufficient to meet the water requirements if harvested and conserved properly. This methodology is time-saving and cost-effective. It can minimize cost of earthwork and can be utilized for the planning of cost effective water resource management.
13 Birhanu, B. Z.; Traore, K.; Gumma, M. K.; Badolo, F.; Tabo, R.; Whitbread, A. M. 2019. A watershed approach to managing rainfed agriculture in the semiarid region of southern Mali: integrated research on water and land use. Environment, Development and Sustainability, 21(5):2459-2485. [doi: https://doi.org/10.1007/s10668-018-0144-9]
Rainfed farming ; Watershed management ; Participatory management ; Water use ; Water conservation ; Soil conservation ; Contour bunding ; Runoff water ; Soil moisture ; Satellite imagery ; Land use ; Land cover mapping ; Semiarid zones ; Agricultural productivity ; Economic analysis ; Stakeholders ; Development programmes / Mali / Kani Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H048703)
https://link.springer.com/content/pdf/10.1007%2Fs10668-018-0144-9.pdf
https://vlibrary.iwmi.org/pdf/H048703.pdf
https://vlibrary.iwmi.org/pdf/H048703.pdf
(5.40 MB) (5.40 MB)
Soil and water conservation (SWC) practices like that of erosion control and soil fertility measures were commonly practiced in the semiarid region of southern Mali since the 1980s. The SWC practices were mainly meant to increase water availability in the subsurface, reduce farm water runoff and gully formation and improve nutrient content of the soil, thereby increasing crop yield. Despite such efforts to promote at scale SWC practices, the landscape of southern Mali is still affected by high rates of runoff and soil erosion and low crop yield in farmers’ fields. Data are lacking on previous beneficial SWC practices that could be adapted for wider application. In this paper, a watershed approach to managing rainfed agriculture is presented to show potential benefits of SWC practices at field and watershed scales. The approach included (1) community participation in establishing and monitoring new sets of hydro-meteorological monitoring stations and field experiments; (2) studying the dynamics and consumptive water uses of different land uses over time; and (3) evaluating the biophysical and economic advantages of SWC practices implemented in the watershed. Results showed that over a period of 34 years (1980–2014) cropping area and consumptive water uses of crops (sorghum and cotton) increased at the expenses of natural vegetation. However, the yield of these crops remained low, indicating that soil fertility management and soil moisture were insufficient. In such cases, implementation of more SWC practices can help provide the additional soil moisture required.
14 Nhamo, Luxon; van Dijk, R.; Magidi, J.; Wiberg, David; Tshikolomo, K. 2018. Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV [Unmanned Aerial Vehicle] capability. Remote Sensing, 10(5):1-12. (Special issue: Remote Sensing for Crop Water Management). [doi: https://doi.org/10.3390/rs10050712]
Irrigated sites ; Remote sensing ; Unmanned aerial vehicles ; Land use mapping ; Land cover mapping ; Satellite imagery ; Landsat ; Farmland ; Vegetation index ; Crops / South Africa / Limpopo Province / Venda / Gazankulu
(Location: IWMI HQ Call no: e-copy only Record No: H048752)
(2.23 MB) (2.23 MB)
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%.
15 Shweta; Bhattacharya, B. K.; Krishna, A. P. 2018. A baseline regional evapotranspiration (ET) and change hotspots over Indian sub-tropics using satellite remote sensing data. Agricultural Water Management, 208:284-298. [doi: https://doi.org/10.1016/j.agwat.2018.06.024]
Evapotranspiration ; Satellite observation ; Remote sensing ; Climate change ; Water use ; Water loss ; Irrigated farming ; Farmland ; Energy balance ; Land use ; Land cover mapping ; Soil moisture ; Rain ; Models / India
(Location: IWMI HQ Call no: e-copy only Record No: H048899)
(5.36 MB)
The annual water loss through evapotranspiration (ET) is an uncertain but significant component of India’s water budget. The present study generated independent estimates of baseline annual ET, calibrated with in situ micrometeorological data over Indian sub-continent, using surface energy balance framework and satellite-based long-term thermal remote sensing, visible and near-infrared observations as the primary data sources. Thirty years’ (1981–2010) of satellite-based ET estimates at 0.08° grid resolution were used to assess trend in regional ET, to find out change hot-spots and probable causes. Long-term collateral data, influencing ET, such as gridded (0.5° × 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) at 25 km resolution from ESA scatterometers and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mean annual ET loss was found to be the highest for Indian cropland (890 Cubic Km) than forest (575 Cubic Km). Annual water consumption pattern over vegetation systems showed declining ET trend at the rate of -16 Cubic Km yr-1 upto 1995 during 30 years which might be due to declining rainfall and solar dimming. This was followed by increasing ET trend (34 Cubic Km yr-1 ). During 2001–2010, irrigated cropland showed a steep increase in water consumption pattern with an average rate of 4 Cubic Km yr-1 while grassland and forest showed declining consumption patterns since 2003 and 2007, respectively thus showing crossover points of their consumption patterns with irrigated cropland. Four agriculturally important Indian eastern, central, western and southern states showed significantly increasing ET trend with S-score of 15–25 and Z-score of 1.09–2.9 during this period. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states, no significant trend in rainfall was observed though significant increase in ET was noticed. Region-specific correlation of annual ET with natural forcing variables was higher for incoming shortwave radiation as compared to rainfall. The increase in ET over irrigated croplands as well as over some of the Indian states could be due to increase in anthropogenic factors which need more detailed investigations in future.
16 Fayas, C. M.; Abeysingha, N. S.; Nirmanee, K. G. S.; Samaratunga, D.; Mallawatantri, A. 2019. Soil loss estimation using RUSLE model to prioritize erosion control in Kelani River Basin in Sri Lanka. International Soil and Water Conservation Research, 7(2):130-137. [doi: https://doi.org/10.1016/j.iswcr.2019.01.003]
Revised Universal Soil Loss Equation ; Estimation ; Soil erosion models ; Erosion control ; Land degradation ; Land use mapping ; Land cover mapping ; River basins ; Slope ; Rain ; Runoff / Sri Lanka / Kelani River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049211)
https://www.sciencedirect.com/science/article/pii/S2095633918301734/pdfft?md5=a3753a3c707e963d96f83f94ed76ed9d&pid=1-s2.0-S2095633918301734-main.pdf
https://vlibrary.iwmi.org/pdf/H049211.pdf
https://vlibrary.iwmi.org/pdf/H049211.pdf
(3.17 MB) (3.17 MB)
Soil erosion contributes negatively to agricultural production, quality of source water for drinking, ecosystem health in land and aquatic environments, and aesthetic value of landscapes. Approaches to understand the spatial variability of erosion severity are important for improving landuse management. This study uses the Kelani river basin in Sri Lanka as the study area to assess erosion severity using the Revised Universal Soil Loss Equation (RUSLE) model supported by a GIS system. Erosion severity across the river basin was estimated using RUSLE, a Digital Elevation Model (15 15 m), twenty years rainfall data at 14 rain gauge stations across the basin, landuse and land cover, and soil maps and cropping factors. The estimated average annual soil loss in Kelani river basin varied from zero to 103.7 t ha-1 yr1 , with a mean annual soil loss estimated at 10.9 t ha1 yr1 . About 70% of the river basin area was identified with low to moderate erosion severity (o12 t ha1 yr1 ) indicating that erosion control measures are urgently needed to ensure a sustainable ecosystem in the Kelani river basin, which in turn, is connected with the quality of life of over 5 million people. Use of this severity information developed with RUSLE along with its individual parameters can help to design landuse management practices. This effort can be further refined by analyzing RUSLE results along with Kelani river sub-basins level real time erosion estimations as a monitoring measure for conservation practices.
17 Zhang, Y.; Chen, G.; Vukomanovic, J.; Singh, K. K.; Liu, Y.; Holden, S.; Meentemeyer, R. K. 2020. Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247:111945. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111945]
Land cover mapping ; Imagery ; Urban development ; Landscape ; Remote sensing ; Semantic standard ; Databases ; Models ; Suburban areas / USA / North Carolina / Raleigh / Durham / Chapel Hill
(Location: IWMI HQ Call no: e-copy only Record No: H049774)
(7.14 MB)
Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.
18 Rana, V. K.; Suryanarayana, T. M. V. 2020. Performance evaluation of MLE [Maximum Likelihood Estimation], RF [Random Forest Tree] and SVM [Support Vector Machine] classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19:100351. [doi: https://doi.org/10.1016/j.rsase.2020.100351]
Watersheds ; Land use mapping ; Land cover mapping ; Hydrology ; Models ; Performance evaluation ; Remote sensing ; Satellites ; Vegetation ; Cultivated land ; Rain ; Machine learning ; Principal component analysis ; Multivariate analysis / India / Gujarat / Vishwamitri Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049839)
(14.80 MB)
The land use and land cover map plays a significant role in agricultural, water resources planning, management, and monitoring programs at regional and national levels and is an input to various hydrological models. Land use and land cover maps prepared using satellite remote sensing techniques in conjunction with landform-soil-vegetation relationships and ground truth are popular for locating suitable sites for the construction of water harvesting structures, soil and water conservation measures, runoff computations, irrigation planning and agricultural management, analyzing socio-ecological concerns, flood controlling, and overall watershed management. Here we use a novel approach to analyze Sentinel–2 multispectral satellite data using traditional and principal component analysis based approaches to evaluate the effectiveness of maximum likelihood estimation, random forest tree, and support vector machine classifiers to improve land use and land cover categorization for Soil Conservation Service Curve Number model. Additionally, we use stratified random sampling to evaluate the accuracies of resulted land use and land cover maps in terms of kappa coefficient, overall accuracy, producer's accuracy, and user's accuracy. The classifiers were used for classifying the data into seven major land use and land cover classes namely water, built-up, mixed forest, cultivated land, barren land, fallow land with vertisols dominance, and fallow land with inceptisols dominance for the Vishwamitri watershed. We find that principal component analysis with support vector machine is able to produce highly accurate land use and land cover classified maps. Principal component analysis extracts the useful spectral information by compressing redundant data embedded in each spectral channel. The study highlights the use of principal component analysis with support vector machine classifier to improve land use and land cover classification from which policymakers can make better decisions and extract basic information for policy amendments.
19 Wei, Y.; Lu, M.; Wu, W.; Ru, Y. 2020. Multiple factors influence the consistency of cropland datasets in Africa. International Journal of Applied Earth Observation and Geoinformation, 89:102087. [doi: https://doi.org/10.1016/j.jag.2020.102087]
Farmland ; Datasets ; Land fragmentation ; Remote sensing ; Land cover mapping ; Moderate resolution imaging spectroradiometer ; Irrigated land ; Vegetation ; Precipitation ; Food security / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H049971)
https://www.sciencedirect.com/science/article/pii/S0303243419310463/pdfft?md5=0684753fd3e8666ecb686aa90c95632d&pid=1-s2.0-S0303243419310463-main.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
(4.01 MB) (4.01 MB)
Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.
20 Assefa, A.; Haile, Alemseged Tamiru; Dhanya, C. T.; Walker, D. W.; Gowing, J.; Parkin, G. 2021. Impact of sustainable land management on vegetation cover using remote sensing in Magera micro Watershed, Omo Gibe Basin, Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 103:102495. [doi: https://doi.org/10.1016/j.jag.2021.102495]
Sustainable land management ; Normalized difference vegetation index ; Watershed management ; Remote sensing ; Satellite imagery ; Datasets ; Land cover mapping ; Hydrological factors ; Rain / Ethiopia / Omo Gibe Basin / Magera Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050722)
https://www.sciencedirect.com/science/article/pii/S0303243421002026/pdfft?md5=adc6f5caeb7b85ee841a993c82269f8c&pid=1-s2.0-S0303243421002026-main.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
(11.20 MB) (11.2 MB)
The hydrological impact of many expensive investments on watershed interventions remains unquantified due to lack of time series data. In this study, remote sensing imagery is utilized to quantify and detect vegetation cover change in Magera micro-watershed, Ethiopia, where sustainable land management interventions have been implemented. Normalized difference vegetation index (NDVI) values were retrieved for the period 2010 to 2019, which encompasses before, during and after the interventions. Mann-Kendal trend test was used to detect temporal trends in the monthly NDVI values. In addition, multiple change-point analyses were carried out using Pettitt’s, Buishand’s and Standard Normal Homogeneity (SNH) tests to detect any abrupt changes due to the watershed interventions. The possible influence of rainfall on changes in vegetation cover was investigated. A significant increasing trend (from 1.5% to 33%) was detected for dense vegetation at the expense of a significant reduction in bare land from 40.9% to 0.6% over the analysis period. An abrupt change in vegetation cover was detected in 2015 in response to the interventions. A weak and decreasing correlation was obtained between monthly rainfall magnitude and NDVI values, which indicates that the increase in vegetation cover is not from rainfall influences. The study shows that the sustainable land management has an overall positive impact on the study area. The findings of this research support the applicability of remote sensing approaches to provide useful information on the impacts of watershed intervention investments.
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