A novel moisture adjusted vegetation index (MAVI) to reduce background reflectance and topographical effects on LAI retrieval.

A new moisture adjusted vegetation index (MAVI) is proposed using the red, near infrared, and shortwave infrared (SWIR) reflectance in band-ratio form in this paper. The effectiveness of MAVI in retrieving leaf area index (LAI) is investigated using Landsat-5 data and field LAI measurements in two forest and two grassland areas. The ability of MAVI to retrieve forest LAI under different background conditions is further evaluated using canopy reflectance of Jack Pine and Black Spruce forests simulated by the 4-Scale model. Compared with several commonly used two-band vegetation index, such as normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, optimized soil adjusted vegetation index, MAVI is a better predictor of LAI, on average, which can explain 70% of variations of LAI in the four study areas. Similar to other SWIR-related three-band vegetation index, such as modified normalized difference vegetation index (MNDVI) and reduced simple ratio (RSR), MAVI is able to reduce the background reflectance effects on forest canopy LAI retrieval. MAVI is more suitable for retrieving LAI than RSR and MNDVI, because it avoids the difficulty in properly determining the maximum and minimum SWIR values required in RSR and MNDVI, which improves the robustness of MAVI in retrieving LAI of different land cover types. Moreover, MAVI is expressed as ratios between different spectral bands, greatly reducing the noise caused by topographical variations, which makes it more suitable for applications in mountainous area

Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China

Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies

Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images

In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that the dynamics of canopy structure are related to available water, climate, and human activities in the form of porosity, leaf area index (LAI), and clumping index (CI). Therefore, it is important to identify the biophysical parameters of savanna ecosystems, and undertake practical actions for savanna conservation and management. The canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, as most remotely sensed methods were developed for homogeneous and closed canopies. Clumping index is a key variable that can represent the clumping effect from spatial distribution patterns of components within a canopy. However, it is a difficult task to measure the clumping index of the moderate resolution savanna pixels directly using optical instruments, such as the Tracing Radiation and Architecture of Canopies, LAI-2000 Canopy Analyzer, or digital hemispherical photography. This paper proposed a new method using hemispherical photographs combined with high resolution remote sensing images to estimate the clumping index of savanna canopies. The effects of single tree LAI, crown density, and herbaceous layer on the clumping index of savanna pixels were also evaluated. The proposed method effectively calculated the clumping index of moderate resolution pixels. The clumping indices of two study regions located in Ejina Banner and Weichang were compared with the clumping index product over China’s landmass

The best fitted VI-LAI relationships and their statistics.

<p>Note: STDV is the standard deviation of each VI corresponding to the whole range of measured LAI. (<i>VI</i>_max–<i>VI</i>_min) is the difference between the maximum and minimum value of each VI corresponding to the whole range of measured LAI. The last column gives the normalized RMSE (i.e., <i>RMSE</i>/(<i>VI</i>_max–<i>VI</i>_min)) for each VI. Values in bold font indicate high performance.</p

The best fitted relationships between LAI and vegetation indices.

<p>The MAVI and three soil adjusted vegetation indices (SAVI, OSAVI, and MSAVI) are compared in the four study areas: (A) Tiantongshan, (B) Maoershan, (C) Hulunbeier, and (D) Xilinhaote. The statistics of the best fitted VI-LAI relationships are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102560#pone-0102560-t003" target="_blank">Table 3</a>. MAVI produces a higher <i>R</i>², smaller normalized RMSE of retrieved LAI compared with the three soil adjusted vegetation index in both forest and grassland areas.</p

The information on the study areas and the Landsat-5 TM images used in this study.

<p>The information on the study areas and the Landsat-5 TM images used in this study.</p

Background reflectance effects on vegetation indices at different LAI values in Jack Pine forest.

<p>The effects of different backgrounds (moss, lichen, and forest soil) on the selected vegetation indices (SAVI, OSAVI, MSAVI, MNDVI, RSR, MAVI, NDVI, and SR) are simulated using the 4-Scale model for the different LAI levels in Jack Pine forest. The forest background strongly affects the values of SAVI, OSAVI, MSAVI, and NDVI as the LAI values are less than 2. MAVI and SR can reduce the effects of forest backgrounds at low LAI values. RSR and MNDVI show the smallest background reflectance effects among these vegetation indices.</p

Effects of slope variations on different vegetation indices.

<p>Note: (A) the coefficient of variation (<i>CV</i>) of each vegetation index varies with slopes, (B) the <i>R</i>² values of linear correlations between vegetation indices and the cosine of the incidence angle vary with slopes. The <i>CV</i> values of MNDVI vary from 5.32% to 13.02% corresponding to the slopes from 5° to 25°, which shows the largest topographical noise among all the selected vegetation indices. RSR has the second largest <i>CV</i> values ranging from 7.09% to 9.81%. SR presents a medium <i>CV</i> values in the range from 3.14% to 5.37%. The <i>CV</i> values are quite small for NDVI and MAVI ranging from 0.64% to 1.92% and from 0.80% to 2.56%, respectively, implying that NDVI and MAVI can remove much of topographic noise through expressing in band-ratio form. The conclusions based on <i>R</i>² are also similar.</p