Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining, agriculture, forest, and urban areas

Wohlfart, Christian; Mack, Benjamin; Liu, Gaohuan; Kuenzer, Claudia

doi:10.1016/j.apgeog.2017.06.004

Cited by 61 publications

(27 citation statements)

References 82 publications

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“…In contrast, remote sensing techniques enable frequent imaging, easier access, and larger-scale monitoring, becoming important global data sources for detecting environmental changes (including those to cropland) [2][3][4]. The moderate resolution of publicly available USGS Landsat data [5] makes them suitable for monitoring changes in agriculture fields [6][7][8]. Detection of cropland change can be based on comparisons between two or more Landsat images [9][10][11], and to date, many studies have applied Landsat time-series analysis to long-term monitoring of cropland change [12,13].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Zhu

Liu

et al. 2019

Remote Sensing

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Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998–2018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km2 was converted to lake and 499.9 km2 was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region.

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Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Zhu

Liu

et al. 2019

Remote Sensing

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“…A set of procedures and measures, known as landscape metrics, allows quantitative understanding and estimating the landscape structure patterns 7 . GIS quantifies the particularities of the landscape 8 and, when incorporated into Remote Sensing, analyzes the physical environment through a geo-referenced database at different dates and scales 9 .…”

Section: Introductionmentioning

confidence: 99%

Geotechnology in the analysis of forest fragments in northern Mato Grosso, Brazil

Barbosa

Spletozer

Roque

et al. 2018

Sci Rep

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Pasture implantation fragments and reduces the Amazonian forest area. The objective was to quantify landscape changes in 1985, 2000 and 2015 in northern Mato Grosso, Brazil. The study was carried out in three scenes obtained by the LANDSAT satellite of a microbasin (2742.33 ha) in the municipality of Alta Floresta. Forest, water bodies, pasture and exposed soil were the thematic classes determined to e mapping the land use evolution. The edge, density and shape indexes of the fragments were measured. Normalized vegetation difference (NDVI) values were high in 1985. Land use and occupation over 15 years (1985–2000) reduced forest cover by 69.8%, but it increased by 1.7% over the next 15 years (2000–2015). The number of exposed soil patches increased between the periods, but the total area and number of the patches of the forest fragments decreased. The high values of NDVI in 1985 showed vegetated areas with high density. Reducing forest cover decreases the size of the fragments, increases the isolation and the number of soil patches exposed. The mapping of land use showed a reduction of the Amazon forest in the microbasin in the north of Mato Grosso, in the years 2000 and 2015 compared to 1985.

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“…Further spectral features such as percentiles derivation, vegetation indices, and a dense set of Landsat imagery were excluded from the analysis. The described data was already proven to be essential to achieving higher accuracy in land cover predictions and change detection assessment [28,37].…”

Section: Forest Cover Change Analysismentioning

confidence: 99%

“…A variety of different spectral-temporal metrics were estimated based on bi-annual Landsat stacks, characterizing different land cover classes for the most prominent phenological information. The procedure follows the approach as described in Mack et al [33] and Wohlfart et al [28]. The lack of temporal regularity of Landsat acquisitions constrains the direct quantification of phenological metrics.…”

Section: Spectral-temporal Landsat Time-series Metricsmentioning

confidence: 99%

“…This permitted to obtain a denser temporal coverage from the study region. Similar to Wohlfart et al [28], Knauer et al [29] and Gebhardt et al [30], the FMASK (Function of mask) algorithm was applied over the Landsat images to identify and mask pixels classified as clouds shadows or no data (see Figure 2). The FMASK algorithm was developed to automatically detect and mask clouds, cloud shadows and snow from Landsat images by taking the spectral and textural features into consideration based on probabilistic scores [31].…”

Section: Landsat Image Acquisition and Pre-processingmentioning

confidence: 99%

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Assessing Forest Cover Dynamics and Forest Perception in the Atlantic Forest of Paraguay, Combining Remote Sensing and Household Level Data

Ponte

Mack

Wohlfart

et al. 2017

Forests

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Abstract:The Upper Parana Atlantic Forest (BAAPA) in Paraguay is one of the most threatened tropical forests in the world. The rapid growth of deforestation has resulted in the loss of 91% of its original cover. Numerous efforts have been made to halt deforestation activities, however farmers' perception towards the forest and its benefits has not been considered either in studies conducted so far or by policy makers. This research provides the first multi-temporal analysis of the dynamics of the forest within the BAAPA region on the one hand, and assesses the way farmers perceive the forest and how this influences forest conservation at the farm level on the other. Remote sensing data acquired from Landsat images from 1999 to 2016 were used to measure the extent of the forest cover and deforestation rates over 17 years. Farmers' influence on the dynamics of the forest was evaluated by combining earth observation data and household survey results conducted in the BAAPA region in 2016. Outcomes obtained in this study demonstrate a total loss in forest cover of 7500 km 2 . Deforestation rates in protected areas were determined by management regimes. The combination of household level and remote sensing data demonstrated that forest dynamics at the farm level is influenced by farm type, the level of dependency/use of forest benefits and the level of education of forest owners. An understanding of the social value awarded to the forest is a relevant contribution towards preserving natural resources.

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Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining, agriculture, forest, and urban areas

Cited by 61 publications

References 82 publications

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Geotechnology in the analysis of forest fragments in northern Mato Grosso, Brazil

Assessing Forest Cover Dynamics and Forest Perception in the Atlantic Forest of Paraguay, Combining Remote Sensing and Household Level Data

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