Measuring Woody Encroachment along a Forest–Savanna Boundary in Central Africa

Abstract Changes in net area of tropical forest are the sum of several processes: deforestation, regeneration of previously deforested areas, and the changing spatial location of the forest–savanna boundary. The authors conducted a long-term (1986–2006) quantification of vegetation change in a 5400 km2 forest–savanna boundary area in central Cameroon. A cross-calibrated normalized difference vegetation index (NDVI) change detection method was used to compare three high-resolution images from 1986, 2000, and 2006. The canopy dimensions and locations of over 1000 trees in the study area were measured, and a very strong relationship between canopy area index (CAI) and NDVI was found. Across 5400 km2 12.6% of the area showed significant positive change in canopy cover from 1986 to 2000 (0.9% yr−1) and 7.8% from 2000 to 2006 (1.29% yr−1), whereas <0.4% of the image showed a significant decrease in either period. The largest changes were in the lower canopy cover classes: the area with <0.2 m2 m−2 CAI decreased by 43% in 20 years. One cause may be a recent reduction in fire frequency, as documented by Along Track Scanning Radiometer-2/Advanced ATSR (ATSR-2/AATSR) data on fire frequency over the study area from 1996 to 2006. The authors suggest this is due to a reduction in human pressure caused by urbanization, as rainfall did not alter significantly over the study period. An alternative hypothesis is that increasing atmospheric CO2 concentrations are altering the competitive balance between grasses and trees. These data add to a growing weight of evidence that forest encroachment into savanna is an important process, occurring in forest–savanna boundary regions across tropical Africa.

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Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽

10.3390/agronomy11050940 ◽

2021 ◽

Vol 11 (5) ◽

pp. 940

Author(s):

Rocío Ballesteros ◽

Miguel A. Moreno ◽

Fellype Barroso ◽

Laura González-Gómez ◽

José F. Ortega

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat Imagery ◽

Canopy Cover ◽

Remote Sensing Data ◽

Multilinear Regression ◽

Area Index ◽

Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

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UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications

OENO One ◽

10.20870/oeno-one.2020.54.4.3647 ◽

2020 ◽

Vol 54 (4) ◽

pp. 1093-1103

Author(s):

Jingyun Ouyang ◽

Roberta De Bei ◽

Sigfredo Fuentes ◽

Cassandra Collins

Keyword(s):

Vegetation Index ◽

Management Practices ◽

Canopy Structure ◽

Area Index ◽

Structure Changes ◽

Grape Quality ◽

Canopy Volume ◽

Canopy Area ◽

Plant Area Index ◽

Canopy Management

Aim: To analyse unmanned aerial vehicle (UAV)-based imagery to assess canopy structural changes after the application of different canopy management practices in the vineyard.Methods and results: Four different canopy management practices: i–ii) leaf removal within the bunch zone (eastern side/both eastern and western sides), iii) bunch thinning and iv) shoot trimming were applied to grapevines at veraison, in a commercial Cabernet-Sauvignon vineyard in McLaren Vale, South Australia. UAV-based imagery captures were taken: i) before the canopy treatments, ii) after the treatments and iii) at harvest to assess the treatment outcomes. Canopy volume, projected canopy area and normalized difference vegetation index (NDVI) were derived from the analysis of RGB and multispectral imagery collected using the UAV. Plant area index (PAI) was calculated using the smartphone app VitiCanopy as a ground-based measurement for comparison with UAV-derived measurements. Results showed that all three types of UAV-based measurements detected changes in the canopy structure after the application of canopy management practices, except for the bunch thinning treatment. As expected, ground-based PAI was the only technique to effectively detect internal canopy structure changes caused by bunch thinning. Canopy volume and PAI were found to better detect variations in canopy structure compared to NDVI and projected canopy area. The latter were negatively affected by the interference of the trimmed shoots left on the ground.Conclusions: UAV-based tools can provide accurate assessments to some canopy management outcomes at the vineyard scale. Among different UAV-based measurements, canopy volume was more sensitive to changes in canopy structure, compared to NDVI and projected canopy area, and demonstrated a greater potential to assess the outcomes of a range of canopy management practices. Significance and impact of the study: Canopy management practices are widely applied to regulate canopy growth, improve grape quality and reduce disease pressure in the bunch zone. Being able to detect major changes in canopy structure, with some limitations when the practice affects the internal structure (i.e., bunch thinning), UAV-based imagery analysis can be used to measure the outcome of common canopy management practices and it can improve the efficiency of vineyard management.

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The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends

Remote Sensing ◽

10.3390/rs13173374 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3374

Author(s):

Xin Chen ◽

Tiexi Chen ◽

Qingyun Yan ◽

Jiangtao Cai ◽

Renjie Guo ◽

...

Keyword(s):

Vegetation Change ◽

Vegetation Index ◽

Southwest China ◽

Vegetation Indices ◽

Climate Extremes ◽

Global Scale ◽

Area Index ◽

Huge Impact ◽

Increasing Trends

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

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The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

Experimental Agriculture ◽

10.1017/s0014479700019402 ◽

1991 ◽

Vol 27 (4) ◽

pp. 423-429 ◽

Cited By ~ 20

Author(s):

R. K. Mahey ◽

Rajwant Singh ◽

S. S. Sidhu ◽

R. S. Narang

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Grain Yield ◽

Spectral Data ◽

Growth And Development ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Canopy Cover ◽

Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

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Testing the Amazon savannization hypothesis: fire effects on invasion of a neotropical forest by native cerrado and exotic pasture grasses

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2012.0427 ◽

2013 ◽

Vol 368 (1619) ◽

pp. 20120427 ◽

Cited By ~ 87

Author(s):

Divino V. Silvério ◽

Paulo M. Brando ◽

Jennifer K. Balch ◽

Francis E. Putz ◽

Daniel C. Nepstad ◽

...

Keyword(s):

Canopy Cover ◽

Fire Effects ◽

Forest Edge ◽

Fire Frequency ◽

Fire Intensity ◽

Stable States ◽

Area Index ◽

Tropical Regions ◽

Pasture Grasses ◽

Transitional Forest

Changes in climate and land use that interact synergistically to increase fire frequencies and intensities in tropical regions are predicted to drive forests to new grass-dominated stable states. To reveal the mechanisms for such a transition, we established 50 ha plots in a transitional forest in the southwestern Brazilian Amazon to different fire treatments (unburned, burned annually (B1 yr ) or at 3-year intervals (B3 yr )). Over an 8-year period since the commencement of these treatments, we documented: (i) the annual rate of pasture and native grass invasion in response to increasing fire frequency; (ii) the establishment of Brachiaria decumbens (an African C 4 grass) as a function of decreasing canopy cover and (iii) the effects of grass fine fuel on fire intensity. Grasses invaded approximately 200 m from the edge into the interiors of burned plots (B1 yr : 4.31 ha; B3 yr : 4.96 ha) but invaded less than 10 m into the unburned plot (0.33 ha). The probability of B. decumbens establishment increased with seed availability and decreased with leaf area index. Fine fuel loads along the forest edge were more than three times higher in grass-dominated areas, which resulted in especially intense fires. Our results indicate that synergies between fires and invasive C 4 grasses jeopardize the future of tropical forests.

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Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Remote Sensing ◽

10.3390/rs13071230 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1230

Author(s):

Simeng Wang ◽

Qihang Liu ◽

Chang Huang

Keyword(s):

Arid Region ◽

Vegetation Change ◽

Vegetation Index ◽

Pearson Correlation ◽

Time Lag ◽

Climate Extremes ◽

Northwest China ◽

Precipitation Extremes ◽

Temperature Extremes ◽

Increasing Trend

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

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Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

Remote Sensing ◽

10.3390/rs13030401 ◽

2021 ◽

Vol 13 (3) ◽

pp. 401

Author(s):

Cadan Cummings ◽

Yuxin Miao ◽

Gabriel Dias Paiao ◽

Shujiang Kang ◽

Fabián G. Fernández

Keyword(s):

Machine Learning ◽

Chlorophyll Content ◽

Vegetation Index ◽

Soil Drainage ◽

Management Information ◽

Area Index ◽

Sensing System ◽

Extreme Gradient Boosting ◽

Split Plot ◽

N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

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Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

Sustainability ◽

10.3390/su13010432 ◽

2021 ◽

Vol 13 (1) ◽

pp. 432

Author(s):

Aru Han ◽

Song Qing ◽

Yongbin Bao ◽

Li Na ◽

Yuhai Bao ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Vegetation Change ◽

Fire Severity ◽

Dynamic Change ◽

Burned Area ◽

Fire Event ◽

Vegetation Dynamic ◽

Area Index ◽

Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

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Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

Water ◽

10.3390/w13101333 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1333

Author(s):

Giuseppe Francesco Cesare Lama ◽

Mariano Crimaldi ◽

Vittorio Pasquino ◽

Roberta Padulano ◽

Giovanni Battista Chirico

Keyword(s):

Riparian Vegetation ◽

Vegetation Index ◽

Arundo Donax ◽

Water Bodies ◽

Giant Reed ◽

Multispectral Images ◽

Drainage Channel ◽

Area Index ◽

Riparian Plants ◽

The Impact

Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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