Global trends in vegetation fractional cover: Hotspots for change in bare soil and non-photosynthetic vegetation

Hill, Michael J.; Guerschman, Juan Pablo

doi:10.1016/j.agee.2021.107719

Cited by 19 publications

(10 citation statements)

References 106 publications

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“…A comparison between the fractional cover and the more common NDVI provides insight into general relationships with a well-studied metric [42,44,45,47,49,[68][69][70].…”

Section: Fractional Cover Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Analyzing annual vegetation dynamics provides key information for determining the overall trends in natural vegetation and agricultural areas that may be subjected to abnormal drought, poor cropland management practices, or abandonment [41,42]. In arid regions, vegetation can be categorized either as photosynthetic (i.e., green leaves, healthy growing crops) or non-photosynthetic (dead/decaying/brown vegetation) [42][43][44][45][46][47][48][49]. Fractional cover algorithms use spectral unmixing [43,[46][47][48] to determine the proportion of photosynthetic vegetation (pv), non-photosynthetic vegetation (npv), and bare soil (bs) (i.e., bare soil or rock) contained within a single pixel of imagery [44,46,49].…”

Section: Introductionmentioning

confidence: 99%

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Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

Wardle,

Phillips

2024

Remote Sensing

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The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

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“…A comparison between the fractional cover and the more common NDVI provides insight into general relationships with a well-studied metric [42,44,45,47,49,[68][69][70].…”

Section: Fractional Cover Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

Wardle,

Phillips

2024

Remote Sensing

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“…GF is a more relevant trait that should be used when describing crop functioning [ 5 ]. The difference between VF and GF is the senescent fraction (SF = VF − GF), sometimes called the nonphotosynthetic fraction [ 6 , 7 ]. For crops, SF depends on both the growth stage and state of the plants.…”

Section: Introductionmentioning

confidence: 99%

SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods

et al. 2022

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Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes. However, some confusion is observed mainly between the background and senescent vegetation, particularly over the dark and bright regions of the images. The U-net model achieves similar performances, with slight degradation over the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent. Finally, the models are used to predict the fraction of three classes over whole images or regularly spaced grid-pixels. Results show that green fraction is very well estimated (R2=0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R2=0.70 and 0.73, respectively) with a mean 95% confidence error interval of 2.7% and 2.1% for the senescent vegetation and background, versus 1% for green vegetation. We have made SegVeg publicly available as a ready-to-use script and model, along with the entire annotated grid-pixels dataset. We thus hope to render segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge or, at least, offering a pretrained model for more specific use.

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“…GF is a more relevant trait that should be used when describing crop functioning [5]. The difference between VF and GF is the senescent fraction (SF=VF-GF), sometimes called non-photosynthetic fraction [6]. For crops, SF depends on both the growth stage and state of the plants.…”

Section: Introductionmentioning

confidence: 99%

SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods

Serouart

Madec

David

et al. 2022

Preprint

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The pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green area index, or to characterize the sanitary state of the crop. We developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest. It is based on a U-net model that separates the vegetation from the background. It was trained over a very large and diverse dataset. The vegetation pixels are then classified using a SVM shallow machine learning technique trained over pixels extracted from grids applied to images. Results show that the SegVeg model allows to segment accurately the three classes, with however some confusion mainly between the background and the senescent vegetation, particularly over the dark and bright parts of the images. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent ones. Finally, the model is used to predict the fraction of the three classes over the grids pixels or the whole images. Results show that the green fraction is very well estimated (R_squared=0.94), while the senescent and background fractions show slightly degraded performances (R_squared=0.70 and 0.73, respectively). We made SegVeg publicly available as a ready-to-use script, as well as the entire dataset, rendering segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge, or at least, a pre-trained model to more specific use.

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Global trends in vegetation fractional cover: Hotspots for change in bare soil and non-photosynthetic vegetation

Cited by 19 publications

References 106 publications

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods

SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods

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