Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Song, Wei

doi:10.3390/su11215951

Cited by 18 publications

(12 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) ALS data allowed for an automated and more accurate identification of AAL in terms of classification accuracy (>90%) and spatial resolution (<1.0 m) than did other RS platforms [53][54][55][56][57][58][59][60][61][62][63][64]. Potential improvements in process of AAL identification may be achieved using some qualitative variable of ALS data (e.g., intensity) or alternatively through multispectral ALS data [65][66][67][68].…”

Section: Discussionmentioning

confidence: 99%

“…This is because they are generally more accessible for large areas than ALS data. Here, an application of spectral or multiscale features extracted from aerial [60] or satellite images, such as GF-2 [61], Quick Bird [62], and Landsat [63], resulted in an overall accuracy of 77-91%. Several studies have also demonstrated the potential of radar data as an alternative to optical images for the identification of AAL.…”

Section: Spatial Identification Of Abandoned Agricultural Landmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

2020

View full text Add to dashboard Cite

Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Spatial Identification Of Abandoned Agricultural Landmentioning

confidence: 99%

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

2020

View full text Add to dashboard Cite

show abstract

“…Landsat time series data has a high spatial resolution (30 m), which has a great advantage in identifying finely fragmented abandoned land in mountainous areas. For example, Song [24] mapped the trajectory of land use change based on Landsat and HJ-1 satellite images, and mapped different types of cropland abandoned in the town of Zhongduo, Chongqing from 2012 to 2017. However, due to the remote sensing image acquisition time and cloud influence, the acquisition of long time series data was limited.…”

Section: Introductionmentioning

confidence: 99%

Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China

Wang

Song

2021

Land

View full text Add to dashboard Cite

As a form of land marginalization, abandoned cropland has an important impact on food security and the effective supply of agricultural products. With rapid urbanization across the world, large areas of cropland are abandoned in some regions, especially in mountainous and hilly areas with poor terrain. Due to the fine fragmentation and scattered distribution of abandoned cropland, and considering differences in the abandoned and fallow time of cropland, it is difficult to extract information using remote sensing technology. Therefore, this paper proposes a change in the detection method for extracting abandoned cropland information based on identifying the annual land use trajectory. Based on Landsat satellite data, annual land use was mapped from 2011 to 2020 in Gaolan County, which is located in the hilly and gully region of the Loess Plateau of China, using the random forest classification method. Subsequently, abandoned cropland information in Gaolan County was extracted, based on the land use change trajectory and analysis of the influencing factors of abandoned land. The results showed that: (1) The overall accuracy of land use interpretation in Gaolan County ranged from 86.44% to 95.45%, from 2011 to 2020, with a kappa coefficient of up to 0.93, and the classification results were ideal. (2) The recall of extracted abandoned cropland was 81%, the extraction accuracy of which was relatively high. (3) From 2013 to 2020, the cropland abandonment rate in Gaolan County ranged from 8.41% to 19.65%, with an average of 14.55%, which increased and then decreased. The abandonment rate was highest in 2015 but it then decreased year by year. The average period of abandoned cropland was 4.2 years. (4) The influence factors of the plot scale explain the difference in the spatial distribution of cultivated land abandonment. The higher the slope condition, the lower the soil nutrient content and the greater the possibility of abandonment.

show abstract

“…Scientist Wei Song wrote that two types of approaches have been adopted to estimate the scale of cropland abandonment in previous research: Household surveys and remote sensing monitoring [4]. Freely accessible satellite images have provided many data sources to map abandoned croplands.…”

Section: Introductionmentioning

confidence: 99%

“…Freely accessible satellite images have provided many data sources to map abandoned croplands. Based on the spatial resolution of satellite images, the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and high-resolution satellite data, such as Quick-Bird, and aerial photographs are generally adopted to identify abandoned croplands [4]. Most countries, including Lithuania, have mosaics of multispectral terrestrial satellite imagery (Sentinel-2 satellite) of their territory, which are ready for analysis and research.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Seasonality on the Multi-Spectral Image Segmentation for Identification of Abandoned Land

2021

View full text Add to dashboard Cite

Areas of agricultural land in Lithuania have decreased from 2005 to 2021 by up to 2.4%. Agricultural lands that are no longer used for their main purpose are very likely to become abandoned and the emergence of such lands can cause a variety of social, economic, and environmental problems. Therefore, it is very important to constantly monitor changes of abandoned agricultural lands. The purpose of the research is to analyse the influence of seasonality on image segmentation for the identification of abandoned land areas. Multi-spectral Sentinel-2 images from different periods (April, July, and September) and three supervised image segmentation methods (Spectral Angle Mapping (SAM), Maximum_Likelihood (ML), and Minimum distance (MD)) were used with the same parameters in this research. Studies had found that the most appropriate time to segment abandoned lands was in September, according to the SAM and ML algorithms. During this period, the intensity of the green colour was the highest and the colour brightness of abandoned lands differed from the colour intensity of other lands.

show abstract

Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Cited by 18 publications

References 60 publications

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China

The Influence of Seasonality on the Multi-Spectral Image Segmentation for Identification of Abandoned Land

Contact Info

Product

Resources

About