Satellite remote sensing of land cover change in a mixed agro-silvo-pastoral landscape in the Alentejo, Portugal

Allen, Harriet; Simonson, William D.; Parham, Erin; Santos, Eduardo de Basto e; Hotham, Paul

doi:10.1080/01431161.2018.1440095

Cited by 22 publications

(31 citation statements)

References 46 publications

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“…In addition, the agricultural areas can be characterized by different cropping calendars (due to different agricultural practices, intensive and super-intensive) and field geometries (with the existence of both fragmented parcels and larger and compact parcels) and can be influenced by the climate conditions and LULC management [18][19][20]. These characteristics, together with the influences of the complex spectral properties of the different vegetation types [32], point to some of the challenges present in a multitemporal remote sensing application.…”

Section: Context and Backgroundmentioning

confidence: 99%

“…Forested areas did vary more over the 21 years analyzed, the area occupied ranged between 11% (maximum area) and 8% (minimum area). In particular, from 2003 to 2010-2011, little or no change in the LULC was detected, perhaps due to the measures brought by the 2003 Common Agriculture Policy (CAP) that more driven by social integration and less to supporting production [32]. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 995 996 997 998 999 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 Different occupation areas for each class were observed, which was in agreement with the LULC class spatial distribution.…”

Section: Lulc Classification and Analysismentioning

confidence: 99%

“…Forested areas did vary more over the 21 years analyzed, the area occupied ranged between 11% (maximum area) and 8% (minimum area). In particular, from 2003 to 2010-2011, little or no change in the LULC was detected, perhaps due to the measures brought by the 2003 Common Agriculture Policy (CAP) that more driven by social integration and less to supporting production [32]. Table 6 presents the error matrix in proportion to the classification map.…”

Section: Lulc Classification Accuracy Summarymentioning

confidence: 99%

“…Interestingly, Allen et al [32] and Senf et al [78] had already mentioned the difficulty of classifying the different LULC types of a region with a heterogeneous biophysical environment due to the wide range of spectral signatures for each LULC class. Our word with the Beja municipal region was no different; the permanent cropland class is an example of a class with a complex spectral pattern.…”

Section: The Lulc Types and Temporal Phenological Signatures Diversitymentioning

confidence: 99%

“…The Alqueva Dam was built in 2002 on the Guadiana River, creating the largest artificial lake in Europe. However, it was only fully operational in 2012 in providing water for agriculture services [32].…”

Section: Brief Introduction To the Study Areamentioning

confidence: 99%

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Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

2019

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The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.

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Section: Context and Backgroundmentioning

confidence: 99%

Section: Lulc Classification and Analysismentioning

confidence: 99%

“…Forested areas did vary more over the 21 years analyzed, the area occupied ranged between 11% (maximum area) and 8% (minimum area). In particular, from 2003 to 2010-2011, little or no change in the LULC was detected, perhaps due to the measures brought by the 2003 Common Agriculture Policy (CAP) that more driven by social integration and less to supporting production [32]. Table 6 presents the error matrix in proportion to the classification map.…”

Section: Lulc Classification Accuracy Summarymentioning

confidence: 99%

Section: The Lulc Types and Temporal Phenological Signatures Diversitymentioning

confidence: 99%

Section: Brief Introduction To the Study Areamentioning

confidence: 99%

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Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

2019

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Mediterranean Forests, Woods and Shrublands

Allen

2019

Geography in Britain After World War II

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Modelling biodiversity trends in the montado (wood pasture) landscapes of the Alentejo, Portugal

et al. 2018

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Context Montados are dynamic agroforestry systems of southern Portugal, with high economic and ecological values. Changes in land use and cover have important implications for landscape-level biodiversity and its conservation. Objectives Our objectives were to evaluate the biodiversity values and trends in a montado system in the Alentejo, Portugal so as to inform landscape level conservation approaches. In doing so, we aimed to develop a replicable and robust approach drawing together field observation, expert opinion, and remote sensing to produce predictions relevant to land management planning.Methods Field sampling and subsequent analysis of data on the birds, butterflies and plants in eight distinct land covers allowed the identification of two principal habitat groupings of importance: 'montado mosaic' and 'shrubland'. Morphological spatial pattern analysis was performed on Landsat-derived GIS habitat layers for 1984 and 2009, generating maps and statistics for change in the different landscape functional classes. In addition, we demonstrated how the modelling of ecotones between open and closed biomes can identify the preferred hunting grounds of the threatened Iberian lynx and black vulture, flagship species whose conservation provides benefits to the area's wider biodiversity values. Results Total and core area of montado mosaics and shrubland increased over the 25 year period, whilst the amount of habitat connectivity declined in the case of shrubland. Considerable local variation in these trends highlighted targetable areas for conservation action (e.g. through agri-environment spending).

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Satellite remote sensing of land cover change in a mixed agro-silvo-pastoral landscape in the Alentejo, Portugal

Cited by 22 publications

References 46 publications

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

Mediterranean Forests, Woods and Shrublands

Modelling biodiversity trends in the montado (wood pasture) landscapes of the Alentejo, Portugal

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