Monitoring cropland abandonment with Landsat time series

Yin, He; Brandão, Amintas; Buchner, Johanna; Helmers, David P.; Iuliano, Benjamin; Kimambo, Niwaeli E.; Lewińska, Katarzyna Ewa; Razenkova, Elena; Rizayeva, Afag; Rogova, Natalia; Spawn, S.; Xie, Yanhua; Radeloff, Volker C.

doi:10.1016/j.rse.2020.111873

Cited by 128 publications

(99 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, it can be challenging to analyze recent abandonment from satellite data due to confusion among active fallow and noncropland classes, and the general difficulty of differentiating short-term idling of land from longer-term abandonment [33][34][35] . Despite these limitations, field-level expected accuracies for all cropland expansion and abandonment ranged from 71.0 to 86.9% for both the user's and producer's perspectives (Supplementary Table 8) and were consistent with targeted standards for measuring change 36,37 .…”

Section: Discussionmentioning

confidence: 99%

Cropland expansion in the United States produces marginal yields at high costs to wildlife

et al. 2020

Self Cite

View full text Add to dashboard Cite

Recent expansion of croplands in the United States has caused widespread conversion of grasslands and other ecosystems with largely unknown consequences for agricultural production and the environment. Here we assess annual land use change 2008–16 and its impacts on crop yields and wildlife habitat. We find that croplands have expanded at a rate of over one million acres per year, and that 69.5% of new cropland areas produced yields below the national average, with a mean yield deficit of 6.5%. Observed conversion infringed upon high-quality habitat that, relative to unconverted land, had provided over three times higher milkweed stem densities in the Monarch butterfly Midwest summer breeding range and 37% more nesting opportunities per acre for waterfowl in the Prairie Pothole Region of the Northern Great Plains. Our findings demonstrate a pervasive pattern of encroachment into areas that are increasingly marginal for production, but highly significant for wildlife, and suggest that such tradeoffs may be further amplified by future cropland expansion.

show abstract

Section: Discussionmentioning

confidence: 99%

Cropland expansion in the United States produces marginal yields at high costs to wildlife

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, this framework can generate maps of CT before and after evolution occurs or over any specified time period. The trends of spectral index over time have provided insights into the change processes in various landscapes [59]- [64].…”

Section: Discussionmentioning

confidence: 99%

State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

Tian

Liu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Detecting the evolution of large-area landscape patterns using long-term remote-sensing images is helpful in supporting research on the relationship between landscape patterns and ecological processes, as well as the development of ecological process simulations and spatiotemporal interaction models. However, detection methods have generally been developed as separate applications, each with a separate type of landscape pattern change; remote-sensing images are acquired at epochal timesteps. Consequently, in practical applications, many omission changes for some types of pattern changes and inaccurate evolution time are presented in the detected map. In this article, stateand-evolution detection models (SEDMs) are promoted to obtain complete information about the evolution of landscape patterns based on yearly land cover data. In the proposed framework, we first define the major categories of landscape pattern changes to comprehensively reveal the characteristics of landscape pattern changes associated with real change cases. Next, a morphological rule-based pattern recognition approach is proposed for quantitative discrimination among these categories. This approach is then applied in annual land cover data to continuously detect landscape pattern evolution processes and evolution time. Finally, the detected evolution time in different evolution processes is applied to measure the timestep between two disparate types. The performances of the SEDMs are presented by Landsat-derived land cover evolution in Shanxi, China. The detected results are indirectly verified by the land cover conversion matrix and connect index, indicating strong robustness and generalization ability of the SEDMs.

show abstract

“…In contrast, precision, recall, and F1-score consider the total number of samples for each class separately. (18) where TP, TN, FP, and FN represent the true positive, true negative, false positive, and false negative predictions, respectively.…”

Section: Performance Measuresmentioning

confidence: 99%

“…For example, Yu et al [13] classified crops using a time-series VI trajectory via the curve-fitting method. Supervised algorithms, such as support vector machine (SVM), random forest (RF), and neural networks (NNs), have shown superior capability in modelling high-degree nonlinear problems in land-use classification [14,15] and cropland abandonment detection [16][17][18]. Löw et al [17] used machine learning algorithms (SVM and RF) to successfully distinguish abandoned cropland from a cropland dataset.…”

Section: Introductionmentioning

confidence: 99%

“…However, abandoned cropland can be easily confused with spectrally similar land types in a regional task. To address this problem, Yin et al [18] developed an RF-based model to detect abandoned cropland using several indices such as the bare soil index (BSI), normalized burn ratio (NBR), and NDVI time series as input. With the boom of computer vision technologies, the classification of the land-use type can be achieved by vision-based approaches using merely mono-temporal remote sensing data [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

et al. 2021

View full text Add to dashboard Cite

Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the K-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from 4.7 × 105 ha to 7.1 × 105 ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately 4 × 105 ha) and the abandoned salinized farmland decreased (reduced from 0.45 × 105 ha to 0.19 × 105 ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization.

show abstract

Monitoring cropland abandonment with Landsat time series

Cited by 128 publications

References 92 publications

Cropland expansion in the United States produces marginal yields at high costs to wildlife

Cropland expansion in the United States produces marginal yields at high costs to wildlife

State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

Contact Info

Product

Resources

About